There are many books that can be called useful. But there are books - breakthroughs, books - revelations, books - bombs. They excite the mind, awaken an intellect exhausted by routine, create hope, and you want to return to them again and again. One of these books, in my opinion, is Doug DeCarlo’s book “ Extreme Project Management».
In 2005, I worked as a PM on a fairly complex project with high risks. The company where I worked had a traditional project management culture based on document templates, calendar plans with Gantt charts, fairly strict budgets, and standard reports. This culture is based on the fact that the requirements for results, budget and schedule must be approved once and any changes to them are undesirable. In this paradigm, the project manager must invent a plan at the very beginning and strictly follow it, as if executing the plan was an end in itself. If you managed not to deviate from the plan, then you are an experienced project manager; if not, you still have a lot to learn. I understood that something was wrong here, but I couldn’t find like-minded people. Much of the literature has been on the side of template management.
And now a powerful blow has been dealt to dogma. It was very unexpected and equally pleasant to meet a like-minded person who risked writing an entire book about the emerging new approach, where common sense and entrepreneurial logic prevailed. I really want many people to read this book. If a book helps shift the old paradigm, a lot in your life and work will change. Doug DeCarlo's views are quite consistent with Lean principles.
Doug DeCarlo says that there is no need to close yourself off from reality with formal plans, budgets, approved procedures, that what is important is not achieving the initially set goal, but searching for and obtaining the desired result. He imagines the extreme project as shooting at a moving target, like a homing missile that searches for a target in real time. Reality rules! says Doug DeCarlo. Extreme project management does not mean doing the project “on your knees”; on the contrary, it is aerobatics. It's like F1 in auto racing. Extreme project management requires special skills and abilities, flexibility, speed of decision-making, and breadth of coverage.
“People would like the world to be a nice, neat place where everything is already certain and predictable. We all know it's not real, but we act like it is, and that's why we get into trouble. In project management, for example, we estimate how much time we will spend on a particular task, then we plug our estimates into a progress planning program and end up with a work plan using the critical path method, which we have already achieved. we treat it as something conditioned. This is absolute nonsense because the program in the minds of senior managers creates the illusion that we have certainty that does not exist, and these false expectations create problems for all of us in the future.”
“We live in a world of quantum projects, in which change and uncertainty are the norm. What then is project management in light of these circumstances?
“What I am truly convinced of is that this book will either cause a huge stir in the project management world or be considered heretical. It can also be the source of a paradigm shift. In any case, you will not remain the same as before after reading this book. You can no longer find solace in your precise work plan using the critical path method, so elegant in its illusion of certainty.”
"Behind Lately The world of project management has changed dramatically and irreversibly. Today's projects simply do not bear even a remote resemblance to yesterday's. The very world in which project management took place has irrevocably sunk into the past.”
"Here main features of an extreme project:
Requirements change overnight.
The project requires the use new technology and new methods that have not been tested by anyone before.
The project completion time (compared to a regular project) has been halved.
The quality of life during the project is more like non-existence.
In the midst of a project, the customer suddenly decides that he wants a different end result.
The environment in which the project exists can be described as chaotic, unpredictable, and randomly changing."
“Traditional project management deals with something known. Extreme projects deal with the unknown. Traditional projects develop slowly and steadily, and are planned methodically. Extreme projects are chaotic, disorderly and unpredictable; speed and innovation are critical, and planning is done at the last minute.”
“Extreme projects are messy. This is reality. But reality exists regardless of the plans made, and we are unable to stop it. She has her own plans. “Reality rules!” . And we can only react to changes. This point is so fundamental and so important to remember that if you are working on an extreme project, I strongly recommend that you write the phrase “ Reality rules!"And do it in a mirror image."
“If you take the time to carefully plan every step, the project will likely lose its relevance when you complete it. During this time, the very problem or opportunity you were studying may change beyond recognition. And since for extreme projects constant change is the rule (and stability is the exception), Yesterday's plans will be no more recent than a month-old fish sandwich».
“Innovation is extremely important for extreme projects. They are the very essence of extreme projects. This requires, first and foremost, the creation of innovative processes and project management methods that result in innovative products and services. You won't be able to cut your project time in half by working twice as hard. This is a hopelessly outdated worldview.”
« Extreme Project is a process of finding the desired result through trial and error. His can be compared to a missile searching for a target using thermal radiation. An extreme project is self-correcting, and you won't have time to discuss every decision with higher management. But even if you find it, people at the top of the hierarchy won't always be available. Project teams need to make urgent and immediate decisions in light of rapidly changing requirements and circumstances. The goal of traditional projects, on the contrary, is to achieve the desired result with maximum efficiency while minimizing deviations from the original plan. Optimization and efficiency are the goal. Project teams achieve the desired result by following predefined processes and rules. Strict control principles are often put in place to ensure that the project does not deviate from cost, quality, or schedule targets. The traditional approach to an extreme project can be compared to trying to full speed ahead car on the expressway while looking in the rearview mirror."
“For extreme projects that are messy in nature, we will focus on efficiency rather than efficiency. We are trying achieve the desired result, which may only vaguely resemble the original target. The golden triangle of traditional project management - getting it done on time, with quality and within budget - will not help in extreme conditions. Why? Yes because Determination of deadlines, quality and budget is constantly changing during the implementation of the project».
« Traditional design looks like a waterfall- with its smoothly descending, sequential Gantt charts and eight levels of detail. Project management using the “waterfall” principle is appropriate in conditions of relatively low work speed and low uncertainty. This model is well suited to traditional MEC projects that have a clear goal and a proven plan to achieve it. The process of closing a nuclear power plant and the project of creating a new McDonald's restaurant can be well represented using a waterfall model.
Mental model of a traditional project
In contrast, extreme projects, characterized by changing requirements and completion dates, unpredictability, chaos, speed and innovation, do not fit into this model. An extreme project is more like twisted, overcooked spaghetti».
Mental model of an extreme project
“I usually offer my clients the following definition of an extreme project: An extreme project is a complex, high-speed, self-correcting enterprise in which people interact in search of a desired outcome under conditions of extreme uncertainty, constant change and extreme stress. ».
“Traditional projects adhere to the classical scheme” ready, aim, fire" On the contrary, in extreme projects we first we shoot, and then we change the trajectory of the bullet. This is the reality in which businessmen, project managers and teams of professionals live. The bureaucracy, clear rules and mechanistic approach that characterize traditional projects are not applicable to extreme projects, where uncertainty, improvisation and spontaneity replace predictability, command and control. It follows that we must use a completely different approach when planning and managing extreme projects - acceptable and adaptable to change».
“When managing extreme projects, we understand that the plan must change according to the state of the outside world. If the world changes tomorrow, then our plan will change too. Change is the norm. The uncertainty is obvious. Stability is a deviation from the norm. Traditional project management is focused on the past. Extreme Project Management is future-proofed ».
“The “ready-aim-fire” approach characterizes a high-speed, fast-paced process. The main focus is on the customer, whose active participation in the project is invaluable. The customer is the main stakeholder and, together with the project manager, constantly guides the progress of the project towards the set goal, which continuously changes and becomes clearer with each iteration.”
“If you don't know the future, why waste time planning for it? Extreme project management doesn't do that.
Traditional project management forces people to serve the process. Extreme project management makes the process serve the people.
Traditional project management is a set of practices, approaches and methods that make people servants of the process. Gantt charts, minutes, reports and other processes are designed to limit the activities of people. Extreme Project Management is based on the premise that people are the key to success: thoughts, emotions and interpersonal connections are the basis of creativity. If the team is demoralized, the project will fall behind schedule, over budget, and result in poor results. Thus, extreme project management places a strong emphasis on quality of life and gives project participants control over the process, rather than the other way around.
Traditional project management centralizes control over people, processes, and tools. In extreme projects, control is distributed evenly.
Traditional management seeks to minimize changes and establish tight control over processes. The manager of extreme projects is aware that it is impossible to manage something unknown and unpredictable using the same methods as before. Trying to force reality to fit the project plan is a waste of time. In a properly organized extreme project, no one is under control. On the contrary, they control everything.
Traditional management challenges the whole world (objects, people, time). In extreme projects, the challenge is primarily to yourself, your attitude, your approach to the world.
Traditional project management strives to get people, budgets, and schedules on track. Extreme Project Management anticipates change by taking a minimalist approach to planning and distributing control.
Traditional project management - leads. Extreme project management - leads the way."
“The cliches of traditional management - working according to plan, minimizing changes, strict control - are purely administrative functions. Traditional project managers are like overseers and are only suitable for managing stable processes. In the world of extreme projects, where planning is minimal and change is constant and unpredictable, the project manager plays more of a leadership role. As will be seen later, a good leader leading an extreme project will allow people to find the optimal solution and perform constant self-correction.”
“From the perspective of today's high-speed, change-prone projects, the traditional world of project management is a relic of the past.”
“The two squares on the left side of the figure represent world of traditional project management- a discipline that was born in the engineering and construction industry. Here the approach to project management is closely related to the scientific world of Newtonian physics. Newton's worldview was based on determinism and reductionism - a paradigm according to which the world can be dissected into a predictable set of cause-and-effect relationships between its individual parts. This is left-brain logical and linear thinking at its finest. It is analytical. This so-called mechanistic approach gave rise to the belief that projects could be planned with a high degree of certainty. He pioneered project management using the waterfall model. But on the other side there is the right hemisphere, which works nonlinearly. Its operating principle is relative and arbitrary, and it solves problems using systems thinking.”
« In the world of extreme projects, a plan is not a dogma. And, unlike Newton's world, extreme projects are subject to the laws of a new science: the world of quantum physics, self-organizing systems and chaos theory."
“Many companies have only recently realized the importance of getting the process called project management right, and are now rushing to embrace the traditional approaches presented by organizations like the Software Engineering Institute (SEI), PMI and others. Unfortunately, these organizations appear to be wasting their time. Bob Kulin, PMP (Project Management Professional), made the following statement: “I have always believed that members of the project management profession do themselves a disservice if they do not recognize that many, if not most, projects do not live up to the fundamental principles established by PMI in the Project Management Body of Knowledge (PMBOK) standard. It's time to open your eyes to reality modern conditions business and find a way to survive and thrive in these new circumstances."
“The Extreme Project Management model consists of sets of rules, values, skills, tools and practices, based on the principle of change and uncertainty, that make up the software and hardware of Extreme Project Management:
Accelerators - principles that give freedom to motivation and innovation.
Shared values are a set of values that establish trust between stakeholders.
Business questions are questions with answers that help frequently and quickly provide valuable results to the customer.
Critical success factors are the skills and tools, as well as organizational support, that play a key role in achieving success.”
« For an extreme project, there is only a general idea of the final goal, and practically nothing is known about the methods for achieving it. It's obvious that A traditional, linear approach to project management simply won't work here.. The standard tools, templates and processes of traditional management are of no practical use to the extreme project manager. Instead, the project manager, together with the customer, selects one or more likely areas of work, studies what is happening and prepares for the next stage. This cycle is repeated several times as the project manager and customer search for a point of convergence between the current result and the stated goal, which has most likely already undergone changes in light of new knowledge and discoveries made during previous iterations. Managing an extreme project can be an exciting and exhilarating challenge for a team, whether it's about beating a market, taking down a major competitor, re-acquiring a major customer, or reviving a dying production line. Extreme project management doesn't have to be a destructive, grueling job in the face of reality - as long as you abandon traditional management methods and embrace the new quantum way of thinking, of course."
“Embracing the world of extreme projects requires us to first make changes in our worldview.”
“Like any software, our brain has “default settings,” that is, a way of thinking. By way of thinking I mean a set of assumptions and assumptions about how the world works. And this is our internal program.”
“Here is a short list of key ideas that you should not forget:
By quantum thinking I mean a worldview that embraces change and unpredictability. Quantum thinking assumes that change is the norm.
The Newtonian or linear worldview considers stability to be a given norm.
Extreme projects must be driven primarily from a quantum thinking perspective.
An attempt to use a quantum approach when managing traditional projects will lead to disastrous results.
Applying Newtonian thinking to an extreme project will ruin it completely.”
“Unlike the Newtonian cause-and-effect way of thinking, extreme project management implies that although the end goal of the project is achievable, it is impossible to predict how we will achieve it. Hence, adaptability is more important than predictability».
“The good news is that the leadership of the organization sooner or later realizes that the old approach is not working. The bad news is that the wrong methods are often chosen to correct the situation. Typically, this process begins with the conclusion that not all employees have mastered the new software and the necessary project methodology. On at this stage Newtonian thinking concludes that if everyone followed the rules, then the company would finally be able to achieve clear and predictable results. “We need to tighten discipline,” they say. In other words, the prevailing philosophy of traditional management is encapsulated in the following words: “ If the method doesn’t work, let’s tighten it up.”».
“Millions of dollars are wasted on training programs and certification of employees in the traditional approach to project management, which only hinders the implementation of extreme projects. As the instability of the project grows, the desire for everything to be linear becomes an obsession and inevitably leads to what I call “linear madness.”
"The reality is that an extreme project is a sinuous line. It looks like the curled up overcooked noodles I mentioned earlier. But many project managers who have gone through the school of classical management hold, even if unconsciously, completely different views on how the project should be implemented. They want the project to look like this:
This ponderous, linear left-brain way of thinking is the root cause of Newtonian neurosis: the desire to build an extreme project along a straight line. Tim Lister, a senior consultant at the Cutter Consortium, calls managers who think this way “straight-line people.” These would-be managers ruthlessly try to dominate every changing element of the project through excessive use of project tools, rules, templates, policies and procedures.”
“They also readily admit their own mistakes. If you could listen to the thoughts of a desperate project manager, his conversation with himself would look something like this: The world doesn't fit my plans. I must not be as good a leader as I thought. I must take additional training in project management. I will try and promise to use more standard templates and tools”».
“The world does not correspond to my plans. Let's think about it. Why on earth should the world conform to the plans of your project? What could be more ridiculous? Newtonian neurosis leads to fruitless attempts to change the world in accordance with your plans, which in itself is a fiction. Who would think of changing reality in accordance with fiction? People suffering from Newtonian neurosis."
"Do not misunderstand me. I believe that project management certification provides a valuable service when applying for a job and certainly adds weight to your resume. So you can proudly show off your PMP certificate to others. If you want, get a tattoo. But don't assume that the tools and concepts you've mastered in training have universal applicability. In extreme projects, most of them are practically useless.”
“Newtonian neurosis is by no means a specific disease of extreme project managers. This insidious affliction is widespread among project sponsors, clients, and senior management who insist on using a linear Newtonian approach to stabilize an unpredictably changing world.
I have met quite a few project managers who believe that they adhere to the quantum way of thinking, although they operate according to the Newtonian model. Their behavior does not correspond to their views, although their intentions are quite noble. This phenomenon, known as “unconscious Newtonianism,” underlies Newtonian neurosis.”
« Extreme projects are like jazz. To the untrained listener, jazz may seem random and chaotic, but it is not. Jazz has structure, and jazz musicians have a huge opportunity for improvisation. Jazz does not have clear guidelines. They also don’t exist in extreme project management.”
“Traditional projects are more reminiscent of classical music. They are managed smoothly. You have to stick to the score, otherwise the conductor will get to you with his baton. However, some organizations are already starting to see the light. They understand that the most difficult projects tend to fail when rigid methods and too many tried-and-tested templates, practices and policies are used.”
“I am not trying to argue that there is no place for rigid classical or Newtonian principles in extreme design. Some components of an extreme project require unconditional rigidity, for example, when testing software or conducting a scientific experiment. It is necessary to use both Newtonian and quantum methods. But to successfully implement an extreme project, quantum thinking must prevail in all aspects of the enterprise.”
« The Newtonian way of thinking is based on the fear of change, the fear of making mistakes.. His main task is the desire to prevent bad things from happening. He's trying to change reality to fit someone else's idea of what it should be. He tries to win by force. Trying to take a traditional approach in an unpredictable environment can be dangerous both for the project itself and for your health and well-being.”
“Managing extreme projects means see the world as it is, in its current state, and don't fight it at every step. After all, when something happens, it already becomes reality. Trying to change reality is the same as trying to change history. It's useless. Instead, we forgive past mistakes, face reality and change our plan accordingly, and nothing else. There is no “Reality Override” button on your computer. In an environment of tight deadlines, constant change, high uncertainty and high complexity, using a traditional approach is tantamount to incapacity.”
“Extreme project management is a new type of thinking and management that corresponds to the nature of projects implemented in conditions of “high turbulence”, rapid change and constant uncertainty. It’s about maintaining control and achieving results in a fast-paced environment.”
“By choosing a change-resistant mindset, you are choosing a worldview that is “in sync” with chaos and unpredictability, and you are focusing on people and relationships rather than tools and processes.
The extreme project manager must direct the flow of thoughts, emotions and attitudes towards the achievement of valuable results."
« I propose to consider the project as a living, changeable organism:
Thoughts find expression in the form of ideas, solutions, new facts, data and achievements. When thoughts and emotions converge at a point of convergence, they find expression in meetings using simple diagrams, in conversations over coffee, in drawing up simple, informal diagrams. They come to life in the form of physical prototypes, drawings, memos, PowerPoint presentations, project plans, project documentation and final design decisions.
Emotions constantly find expression in physical and bodily forms: when people frown or smile, when they send an angry email, or celebrate the victory associated with the appearance of the first successful results of experimental work. In contrast, traditional management largely relies on a mechanistic (read Newtonian) approach and refuses to pay attention to the human factor. Its intellectual foundation consists of practices, procedures and policies that make people servants of the process. Can we afford to dehumanize the project? No, not in the quantum world.
Relationships represent a complex web of communication that arises when new information appears, including thoughts and emotions exchanged between project participants. When you look at the results of a project, you see the sum of thoughts, emotions and relationships translated into physical form.”
“Thus, a project is a process by which thoughts and emotions take a certain form. You can look at the desired outcome of a project as something in the making. And with the increase in the volume of thoughts and emotions that the project participants exchange among themselves, the final result takes on more and more distinct outlines.”
“One of the most important tasks of extreme project management is the compression of the time period necessary for thoughts, emotions and relationships to manifest themselves in physical form.”
“Project management, extreme or traditional, is not simply the process of developing and implementing a new product or improving the performance of a service that the customer sought. This also does not mean creating all kinds of artifacts (Gantt charts, journals, reports and other countless documentation). It is much more than that: project management is the science and art of channeling the flow of thoughts, emotions and relationships to achieve meaningful results."
“If projects are people (their thoughts, emotions and relationships), then relationship management becomes main task extreme project leader. People are a key factor in the success of an extreme project.”
“Anyone who participates in a project or is affected by it (before or after completion) is a participant. Project participants provide vital products and services to the project, including leadership, execution of other projects, information, feedback, manpower, collaboration, decisions, approvals, and advice. Projects that depend on your project are also participants in it.”
“You also have to deal with factors internal to the organization. They include systems, policies and procedures (based on Newtonian thinking, of course) that you will have to live with until you find a guardian angel who will help you avoid these annoying obstacles. Organizational culture - the way work is done in a particular organization - can also have a significant impact on you. If you find yourself in a command-and-control organization, you are unlikely to be able to rely on collective decision making, which is a key success factor for an extreme project.”
« Make friends with change . Changes have a negative impact on the project. They disrupt the order of things. Change is generally viewed with caution, which is the reason why traditional management places so much emphasis on change management. Extreme project management requires a different attitude towards change - one in which changes are perceived as new opportunity , and accepting change increases the chances of achieving the desired result (which may differ significantly from what was planned).”
« Play on people's passions . I don't think many people are excited in the morning to think about getting back to work on a project. In fact, the word project contains some depressing connotations. People will work more enthusiastically if they know they are fulfilling a specific mission; if they view the project not as a “project”, but as a reason for their actions. You have to show people that their work is part of something bigger, giving them a clear idea of ends and means.”
« Keep it simple . For an extreme project, the good old principle “less is more” is not an empty phrase. It is very serious. In practice, less becomes more: fewer processes, less management, fewer policies and standard procedures.”
“The primary goal of an extreme project manager is to achieve and maintain commitment to the project mission. You can safely talk about establishing commitment when team members are highly motivated and most of the community of project participants supports you.”
“When commitment wanes or disappears, the energy field of the project declines and the project falls into a gloomy mood. Immediately there is a risk of non-compliance with time frames, loss of quality, failure of financial expectations and complete failure of the project.”
“Self-discipline is the first critical success factor in extreme project management. In the case of an extreme project, this means the ability to self-govern in hostile conditions. Cannot be stabilized the world, you can only stabilize your condition. This is your only opportunity. And when you stabilize yourself, the world around you, as if by magic, becomes more stable. If you don't exercise self-discipline when working in a hostile environment, you will set yourself up for suffering."
“Commitment is the positive energy, the elevated feeling that permeates a project and propels it towards success. Indifference or ridicule is negative energy that slows down the development of the project.”
“Nine reasons why an extreme project manager fails.
Extreme project managers fail when they look inward to the project and focus on technical details and product development (content), forgetting about the environment surrounding the project: the general economic situation, the expectations of the participants and the emotional state of the project. This creates unresolved conflicts, resulting in loss of commitment and failure to produce an acceptable end product or service. The following error factors for project managers are primarily related to the project environment. They are found in all projects, but take on special significance in extreme conditions:
1. Lack of a benefactor - failure to find a suitable sponsor who would have champion qualities and the ability to crush obstacles.
2. Weak communication skills (communication, negotiation, conflict resolution, support and influence).
3. Hermit Crab Syndrome: The project manager sits in front of a computer screen instead of sitting in front of key participants.
4. Good soldier syndrome: excessive softness; bowing to leadership and surrendering one’s positions; easy execution of orders.
5. Loss of business focus: misapplication of the four business questions (which will be covered in the next chapter):
Invasion of someone else's territory: an attempt to answer the first question of business (“ Who needs this and why?"). This question must be answered by the project sponsor.
Fleeing the battlefield: fear of taking responsibility for answering the second business question (“ What needs to be done for this?”, allowing the project sponsor to manage the budget. This is the prerogative of the project manager.
Excessive timidity: failure to obtain what will lead the project to success (third business issue - “ Can we handle this?"). You must be able to negotiate.
Malicious obsequiousness: The project manager continues to perform work with a negative answer to the fourth business question (“ It's worth it?"). This is the same as initiating a project or continuing to implement it, knowing that it has no chance of success. At the same time, blaming the project manager for mistakes, they forget about the real reason for the failure: the economic justification for the project turned out to be unviable.
6. Incorrect methodology: using an anti-productive methodology for project implementation.
7. Totalitarianism (or cookie-cutter management): The project manager believes that he can manage the dynamics of an extreme project by forcing people to fill out reporting forms, rather than focusing on unlocking motivation, creating innovation, and establishing trusting relationships, which requires a management style based on values and principles.
8. Naive obsequiousness: failure to understand that completing a project does not solve the problem at hand.
9. Out of your element: Lack of understanding that extreme project management (and perhaps any project management) is a job in which one can make the best use of one's innate talents and motivational abilities.”
“The key to running effective group meetings is in your ability to manage the energy of participants, not time. “Let's forget about feelings and emotions,” said one project manager in the middle of a meeting. This was an incorrect proposal. As a professional mediator with thirty years of experience, I can say that my most important skill is the ability to openly deal with the feelings of participants. Model " Feelings -> Facts -> Decisions” plays an important role throughout the meeting. If group members are in a bad mood, don't expect to progress until you address their feelings."
“People often make the mistake of thinking that the difference between traditional and extreme project management is the presence or absence of planning. This idea is far from the truth. Managing both types of projects involves planning, and in both cases the goal is to maintain control of the project.”
“Another fundamental difference between traditional and extreme project management is that traditional project management starts from the design stage of a project and ends at the implementation stage, while extreme project management covers the project much more broadly - from idea to economic effect».
“Extreme projects develop in “flexible organizations,” i.e. in organizations with a change-responsive, project-friendly culture that recognizes and meets the unique needs of projects ranging from extreme to traditional.
“Projects are like flowers. If the soil is poisoned, one or two flowers will survive, but the rest will sooner or later die.”
“The bureaucracy, clear rules and mechanistic Newtonian approach that characterize traditional projects are not applicable to extreme projects, where uncertainty, improvisation and spontaneity displace predictability and control. Extreme projects require a new worldview and a new management model that will allow project managers and business people to maintain control over the situation in changing conditions. The management model must be focused on profitability, without losing sight of quality of life.”
“Managers whose worldview is aimed at strengthening the mechanics of the project make a serious mistake. They strive to develop a strict plan and strictly implement it. But in a world of extreme projects that are subject to influence from competitors, government regulations, changing consumer preferences and new technologies, yesterday’s plans will be no more relevant than a month ago’s newspaper.”
“The reasons for the success of extreme projects are, first of all, competent management of the dynamics, and not the mechanics of the project.”
“The situation is made even worse by sending employees, sometimes in large numbers, to project management training and certification courses that teach traditional project management techniques that only lead to reduced productivity on volatile and inherently complex extreme projects. The result is a waste of time and money.”
“The combination of Newtonian thinking, totalitarianism and project bureaucracy leads to the fact that the project ends up in a straitjacket. Such practices stifle motivation and innovation, which are vitality extreme project. Instead of the desired increase in productivity, the organization faces dysfunction as people begin to work for the system rather than the other way around.”
“In the world of traditional project management, success is defined by outdated principles of meeting schedules, budgets, and all the requirements set during the planning phase. In the world of extreme projects, these success metrics are meaningless. What is the use of meeting all the criteria if the project becomes unprofitable after its implementation? Of course, extreme project managers worry about schedule, budget, goals, and quality, but they also understand that these factors do not determine the success of the project.”
“The Quantum Leader sees his project as follows:
The main secret to maintaining control over an extreme project is to don't try to stretch it along a straight line. This desire is inherently wrong. Instead, it is necessary to establish project boundaries and create multiple checkpoints. Boundaries provide the opportunity to improvise within given limits.”
“The Newtonian leader wants to see his project as follows:
This type of thinking disconnects us from reality because it contradicts it. Newtonian thinking encourages us to stick to a set plan at all costs and promotes the creation of practices and systems that resist or seek to minimize change. Newtonian head tries to change reality in accordance with the plan and controls people according to the principle of obedient submission. But reality rules. For the Newtonian personality, efficiency is more important than results (read: profit). He asks himself: “Is the project over time and budget?”
“Traditional metrics are insufficient because they are tied to schedule, budget, requirements and quality, rather than to the alignment of project results with business values. If a project is on time and on budget, but is not profitable and does not meet basic requirements, its practical value is close to zero.”
“Extreme projects do not live in complete isolation. They are related to other projects and global business issues.”
“This is a book about how to change the world around you... disguised as a book about extreme project management. And this is the basic concept of project management: changing the world around us with each new project. When it comes to change, whether you're a project manager, sponsor, or executive director of an organization, extreme project management levels the playing field. When reality changes, it doesn't care about your position, place of residence or the amount of money in a bank account. Change sets different priorities.”
“The world we live in has long been considered extreme. Nobody can change reality. We can only hope that the most important thing we can do in an extreme world for ourselves and the people around us is changing our type of thinking and accepting a new worldview, a new quantum reality».
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Name: Extreme Project Management.
Extreme Project Management is a flexible and dynamic model for any type of project that is characterized by high speed and uncertainty, and in which failure is not an option.
The book Extreme Project Management gives practical recommendations for managers working with high risks and under intense pressure to achieve the expected end result. Based on Doug DeCarlo's extensive experience working with over 250 project teams, his Extreme Project Management model is built on a set of agreed upon principles, values, skills, tools and practices that have proven to perform well in the face of constant change and uncertainty.
In a world where new technologies are developed and implemented at a breakneck pace, we are increasingly faced with new types of projects. It seems that the world is literally covered by them. These are projects where deadlines are critical, the cost of an error is extremely high, requirements change chaotically and unpredictably, and the customer at the last moment may decide that he actually needs a completely different result. There is uncertainty in everything, sometimes there is too much of it, it is managed by special people - managers of extreme projects in “project-crazed” companies.
To manage the unknown, traditional project management, based on careful planning and clear processes, cannot be used; this approach is working worse and worse, and on some projects it does not work at all, says Doug DeCarlo. It is necessary to accept high uncertainty as the norm, learn to exist in this changing world and add “quantum” thinking to the traditional “Newtonian” project management tools.
CONTENT
Preface to the Russian edition.
The project is jazz 11
Preface 13
Introduction. See the light 17
What is the difference between extreme projects 20
Ready, Fire, Aim! 23
Extreme Project Management 2 5
Paradigm change 27
Part one: New reality 31
1 Applying quantum thinking to extreme reality 33
Is there a method to your madness? 35
Linear Madness 37
Newtonian neurosis and extreme project management 39
Self-diagnosis tools 41
Are you responsible for your words? 43
This is jazz, not classical 44
Towards peaceful coexistence 45
Conclusion 4b
2 Extreme model of success 49
Keys to success 49
What is a "project"? New definition 51
What is "project management"?
New definition 53
What is an "extreme project"? 56
What is "extreme project management"? 56
How to measure the success of an extreme project? 59
Who determines the success of a project? 60
What are the core elements of the extreme model of success? 62
Tools, skills and conditions for achieving success:
5 Critical Success Factors 67
Part Two: Leadership Skills in an Extreme World 71
3 Leadership Begins with Self-Discipline 75
Design-mad organizations 76
Self-torture formula 78
Self-discipline formula 82
Appeal to higher authorities 98
4 The role of a leader for an extreme project manager 103
The role of the extreme project manager 104
Participants: Managing the project environment of an extreme project 112
You as a process leader 118
Nine reasons why an extreme project manager fails 129
You are much stronger than you can imagine 131
If adherence cannot be achieved 135
5 Principles, values and interpersonal skills for the project leader 139
4 Accelerators: How to Unleash Motivation and Foster Innovation 141
10 Shared Values: How to Establish Mutual Trust to Achieve Success 146
4 Business issues: how to ensure that the customer receives valuable results at every stage 150
Development of interpersonal communication skills in an extreme world 152
Principles effective communication 159
How to negotiate 165
Conflict resolution 178
If all else fails 180
6 Extreme Team Management 183
Process values 184
Command Description 186
Creating a Core Team 188
Creating conditions for successful team work 197
Rules for conducting effective meetings 210
Facilitator skills 216
Decision Making and Problem Solving 220
How to earn the right to become a process leader 227
7 Managing participants in an extreme project 233
Difficulties in managing participants 234
Business values 237
Relationship Management 238
Universe of participants 238
Managing project participants 244
Role of the management committee 258
How to fight the illusory cycle of statements 260
Change management: you created it, but will it stick? 261
Business Question Four: Is it worth it? 269
Part Three: Agile Project Model 271
8 Project vision: understanding the sponsor’s vision of the project 279
The answer to the first business question: who needs it and why? 280
First meeting with a sponsor 284
Start of work on the Project Charter 295
Second meeting with sponsor 304
9 Developing a Project Vision: Creating a Collective Vision 311
Preparing for the third meeting with the sponsor 312
Receipt or failure to obtain permission: third meeting with the sponsor 320
Preparation for the framework meeting 327
Holding a framework meeting 332
After the meeting 346
10 Project Evaluation: Planning Meeting 357
Preparing for the Planning Meeting 359
Twelve Steps to a Planning Meeting ST 1
11 Project evaluation: work carried out after planning is completed 397
Project Management Infrastructure Assessment 399
Assessment of financial requirements 400
Stage 12 Project Update: learning by doing 413
Main driving forces 414
Compiling time blocks 418
Application of the IPSSR 420 model
Purpose of Project Update Phase 432
13 Re-evaluating the project: determining the fate of the project 443
What the revaluation of Project 44b is not
Revaluation process 447
14 Project implementation: obtaining economic benefits 467
What happened to the fourth question of business: is it worth it? 470
Moment of transmission of result 472
Stabilization period 473
Project Review Meeting 474
Realization of benefits 477
Part Four: Project Environment Management 489
15 Real-time communication 491
What are the main communication needs of project participants? 495
What are the main characteristics of a viable real-time communication system? 497
What does a real-time communication system consist of? 499
Where can I find acceptable solutions to get started quickly? 502
What are the technical requirements for
to planning and conducting virtual meetings? 506
What do you need to know about planning and conducting web conferences? 509
How to avoid falling into a trap? 510
16. Agile Organization: Management Briefing 513
New dynamics of the 515 project
How organizational leadership can undermine effective project management 517
The role of the 520 project sponsor
Agile Organization: Worst and Best Approaches 523
Reaching agreement 538
Transition period 540
The world is becoming more extreme 541
Afterword by Robert K. Wysocki 543
Extreme means and methods 547
Means and methods of self-discipline 547
Interpersonal tools and skills 5b3
Facilitator techniques 572
Project Management Tools 580
References 583
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1. Extreme control systems
Extreme control systems are those self-propelled guns in which one of the performance indicators must be kept at a maximum level (min or max).
A classic example of extreme control is the automatic frequency control system of a radio receiver.
Fig.1.1 - Amplitude-frequency response:
1.1 Statement of the problem of synthesis of extremal systems
Objects are described by equations:
An extreme characteristic drifts over time.
It is necessary to select a control action that would allow one to automatically find the extremum and keep the system at this point.
U: extr Y=Y o (1.2)
Fig.1.2 - Static extreme characteristic:
It is necessary to determine the control action that ensured the fulfillment of the property:
1.2 Extremum condition
A necessary condition for an extremum is the equality of the first partial derivatives to zero.
A sufficient condition for an extremum is the equality of the second partial derivatives to zero. When synthesizing an extremal system, it is necessary to estimate the gradient, but it is impossible to estimate the vector of second partial derivatives, and in practice, instead of a sufficient condition for an extremum, the relation is used:
Stages of synthesis of an extremal system:
Gradient estimation.
Organization of movement in accordance with the condition of movement towards an extremum.
Stabilization of the system at the extremum point.
Fig. 1.3 - Functional diagram of an extreme system:
1.3 - Types of extreme characteristics
1) Unimodal extremal characteristic of the module type
Rice. 1.4 - Extreme characteristics of the module type:
2) Extreme characteristic of the parabola type
Rice. 1.5 - Extreme characteristic of the parabola type:
3) B general case the extreme characteristic can be described by an nth order parabola:
Y = k 1 |y-y o (t)| n + k 2 |y-y o (t)| n -1 + …+k n | y-y o (t)| + k n +1 (t).(1.9)
4) Vector-matrix representation:
Y = y T By(1.10)
1.4 Methods for estimating gradient
1.4.1 Method of dividing derivatives
Let's consider it on a unimodal characteristic, y is the output of the dynamic part of the system.
yR 1 , Y = Y(y,t)
Let's find the total derivative with respect to time:
With a slow drift, thus
Advantage: simplicity.
Disadvantage: at small 0, the gradient cannot be determined.
Differentiating filter.
Rice. 1.6 - Scheme for estimating the partial derivative:
1.4.2 Discrete gradient estimation
Rice. 1.7 - Scheme for discrete estimation of partial derivative:
1.4.3 Discrete gradient sign estimation
For a small sampling step, we replace:
1.4.4 Synchronous detection method
The synchronous detection method involves adding an additional low-amplitude, high-frequency sinusoidal signal to the input signal to an extreme object and separating the corresponding component from the output signal. Based on the phase relationship of these two signals, we can conclude the sign of the partial derivatives.
Rice. 1.8 - Functional diagram for estimating the partial derivative:
Rice. 1.9 - Illustration of the passage of search oscillations to the system output:
y 1 is the operating point, and the phase difference of the signals is 0.
y 2 is the phase difference of the signals; a multiplying block can be used as a simplest FNC.
Rice. 1.10 - Illustration of the operation of the FNC:
A filter averaging over a period is selected as a filter, which allows one to obtain an output signal proportional to the value of the partial derivative.
Rice. 1.11 - Linearization of the static characteristic at the operating point:
Therefore, the equation of an extremal curve can be replaced by the equation of a straight line:
Signal at the output of the FNC:
k - coefficient of proportionality - tangent of the angle of inclination of the straight line.
Filter output signal:
Thus:
The synchronous detection method is suitable for determining not only one partial derivative, but also the gradient as a whole, with several oscillations of different frequencies being supplied to the input. The corresponding output filters highlight the response to a specific search signal.
1.4.5 Special gradient estimation filter
This method involves introducing a special dynamic system into the system, the intermediate signal of which is equal to the partial derivative.
Rice. 1.12 - Scheme of a special filter for estimating the partial derivative:
T - filter time constant:
A DF differentiating filter is used to estimate the total derivative of Y, and then this estimate of the total derivative is used to estimate the gradient.
1.5 Organization of movement towards an extremum
1.5.1 First order systems
We organize the control law proportionally to the gradient:
Let's write the equation of the closed system:
This is an ordinary differential equation that can be studied using TAU methods.
Consider the statics equation of the system:
If we ensure the stability of a closed-loop system using the gain k, then automatically in static conditions we will reach the extremum point.
In some cases, using the coefficient k, in addition to stability, it is possible to ensure a certain duration of the transition process in a closed system, i.e. ensure the specified time for reaching the extremum.
Where k is stability
Rice. 1.13 - Functional diagram of a first-order gradient extremal system:
This method is only suitable for unimodal systems, i.e. systems with one global extremum.
1.5.2 Heavy ball method
By analogy with a ball that rolls into a ravine and overshoots the points of local extrema, an AC system with oscillatory processes also overshoots local extrema. To ensure oscillatory processes, we introduce additional inertia into the first-order system.
Rice. 1.14 - Illustration of the “heavy” ball method:
Closed-loop equation;
Characteristic equation of the system:
The smaller d, the longer the transition process.
Analyzing the extreme characteristic, the necessary overshoot and duration of the transition process are set, from which the following are set:
1.5.3 Single-channel general systems
Control Law:
Substituting the control law into the control of the object, we obtain the equation of the closed system:
In the general case, to analyze the stability of a closed-loop system, it is necessary to use the second Lyapunov method, with the help of which the gain of the controller is determined. Because The 2nd Lyapunov method provides only a sufficient condition for stability, then the chosen Lyapunov function may turn out to be unsuccessful and a regular procedure for calculating the controller cannot be proposed here.
1.5.4 Systems with the highest derivative in control
General case of extremum of objects:
The functions f, B and g must satisfy the conditions for the existence and uniqueness of a solution to the differential equation. The function g - must be repeatedly differentiable.
C - matrix of derivatives
The synthesis problem is solvable if the product matrix is non-singular, i.e.
Analysis of the solvability conditions for the synthesis problem allows us to determine the derivative of the output variables, which clearly depends on the control action.
If condition (1.31) is satisfied, then such a derivative is the first derivative, and therefore the requirements for the behavior of a closed system can be formed in the form of a differential equation for y of the appropriate order.
Let's form a control law for a closed-loop system, for which we form a control law by substituting in the right side of the control for:
Closed-loop equation with respect to the output variable.
Let's consider the situation when
With the appropriate choice of gain, we obtain the desired equation and automatic access to the extremum.
The controller parameters are selected based on the same considerations as for conventional self-propelled guns, i.e. (SVK) i = (20*100), which allows us to provide the appropriate error.
Rice. 1.15 - Diagram of a system with the highest derivative in control:
In a system, a differentiating filter is introduced into the system to estimate the total time derivative, so it is convenient to use a gradient estimation filter to estimate gradients in such systems. Because Both of these filters have small time constants, then processes of different tempos can arise in the system, which can be identified using the method of separating movements, and slow movements will be described by equation (1.34), which corresponds to the desired at. Fast movements need to be analyzed for stability, and depending on the ratio of the time constant of the DF and the filter for estimating partial derivatives (PDE), the following types of movements can be distinguished:
1) The time constants of these filters are comparable.
The rapid movements describe the combined processes in these two filters.
2) The time constants differ by an order of magnitude.
In addition to slow movements, fast and ultra-fast movements corresponding to the smallest time constant are observed in the system.
Both cases must be analyzed for stability.
2. Optimal systems
Optimal systems are systems in which a given quality of work is achieved through maximum use of the capabilities of the object, in other words, these are systems in which the object operates at the limit of its capabilities. Let us consider a first-order aperiodic link.
For which it is necessary to ensure the minimum transition time y from the initial state y(0) to the final state y k . The transition function of such a system for K=1 looks like this:
Rice. 2.1 - Transition function of the system at U= const:
Let's consider the situation when we apply the maximum possible control action to the input of the object.
Rice. 2.2 - Transition function of the system at U=A= const:
t 1 - the minimum possible time of transition y from the zero state to the final state for a given object.
To obtain such a transition, there are two control laws:
The second law is more preferable and allows for control in the event of interference.
Rice. 2.3 - Block diagram of a system with a feedback control law:
2.2 Statement of the problem of synthesis of optimal systems
2.2.1 Mathematical model of the object
An object is described by state variables
Where the function f(x,u) is continuous, differentiable with respect to all arguments and satisfies the condition of existence and uniqueness of a solution to the differential equation.
This function is nonlinear but stationary. As special cases, the object can take the form of a nonlinear system with additive control:
Or a linear system
The object must be presented in one of the three forms presented above.
2.2.2 Multiple initial and final states
The problem of optimal transition from the initial state to the final state is a boundary value problem
Where the starting and ending points can be specified in one of four ways, shown in Fig. 2.4.
a) problem with fixed ends,
b) problem with a fixed first end (fixed starting point and set of final values),
c) a problem with a fixed right end,
d) problem with moving ends.
Fig. 2.4 - Phase portraits of the transition of the system from the initial state to the final state for various tasks:
For an object, the set of initial states can generally coincide with the entire set of states or the workspace, and the set of final states is a subspace of the set of states or the workspace.
Example 2.1 - Is it possible to transfer an object described by a system of equations to any point in state space?
Substituting the value U from the first equation into the second equation u = x 2 0 - 2x 1 0, we get -5x 1 0 + x 2 0 = 0;
We obtained a set of final states described by the equation x 2 0 = 5x 1 0 ;
Thus, the set of final states specified for an object (system) must be realizable.
2.2.3 State and control restrictions
Rice. 2.5 - General view of the state space working area:
A working area of the state space is allocated and specified. Typically, this area is described by its boundaries using modular conventions.
Fig.2.6 - View of the state space working area defined by modular conventions:
U is also specified - the range of permissible values of the control action. In practice, the region U is also specified using modular relations.
The problem of synthesizing an optimal controller is solved subject to control restrictions and a limited resource.
2.2.4 Optimality criterion
At this stage, the requirements for the quality of operation of the closed-loop system are specified. The requirements are specified in a generalized form, namely in the form of an integral functional, which is called the optimality criterion.
General view of the optimality criterion:
Particular types of optimality criterion:
1) optimality criterion, ensuring a minimum time of the transition process (the problem of optimal performance is solved):
2) optimality criterion that ensures minimum energy consumption:
For one of the components:
For all variable states:
For one control action:
For all control actions:
For all components (in the most general case):
2.2.5 Result form
It is necessary to stipulate in what form we will look for the control action.
Two options for optimal control are possible: u 0 = u 0 (t), used in the absence of disturbance, u 0 = u 0 (x), optimal control in the form of feedback (closed-loop control).
Synthesis problem formulation optimal system in general:
For an object described by variable states with given restrictions and a set of initial and final states, it is necessary to find a control action that ensures the quality of processes in a closed system that corresponds to the optimality criterion.
2.3 Dynamic programming method
2.3.1 Optimality principle
Initial data:
It is necessary to find u 0:
Rice. 2.7 - Phase portrait of the system’s transition from the initial point to the final point in state space:
The trajectory of transition from the starting point to the final point will be optimal and unique.
Statement of the principle: The final section of an optimal trajectory is also an optimal trajectory. If the transition from the intermediate point to the final point were not carried out along an optimal trajectory, then it would be possible to find its own optimal trajectory for it. But in this case, the transition from the starting point to the final point would take place along a different trajectory, which should be optimal, but this is impossible, since there is only one optimal trajectory.
2.3.2 Bellman's basic equation
Let's consider a control object of arbitrary type:
Consider a transition in state space:
Rice. 2.8 - Phase portrait of the system’s transition from the starting point to the final point x(t) is the current (starting) point, x(t+Дt) is the intermediate point.
Let's transform the expression:
Let's replace the second integral with V(x(t+Дt)):
For a small value of Dt, we introduce the following assumptions:
2) Let's expand the auxiliary function
Carrying out further transformations, we get:
Where min V(x(t)) is the optimality criterion J.
As a result we got:
Let's divide both sides of the expression by Dt and eliminate Dt to zero:
We obtain the basic Bellman equation:
2.2.3 Calculation ratios of the dynamic programming method:
The basic Belman equation contains (m+1) unknown quantities, because U 0 R m , VR 1:
Having differentiated m times, we obtain a system of (m+1) equations.
For a limited range of objects, solving the resulting system of equations provides exact optimal control. This problem is called the AKOR problem (analytical design of optimal controllers).
Objects for which the AKOR task is considered must meet the following requirements:
The optimality criterion must be quadratic:
Example 2.2
For an object described by the equation:
It is necessary to ensure the transition from x(0) to x(T) according to the optimality criterion:
Having analyzed the object for stability, we obtain:
U 0 = U 2 = -6x.
2.4 Pontryagin's maximum principle
Let us introduce an extended state vector, which we expand due to the zero component, for which we choose the optimality criterion. zR n+1
We also introduce an extended vector of right-hand sides, which we expand using the function under the integral in the optimality criterion.
Let us introduce Ш - vector of conjugate coordinates:
Let us form the Hamiltonian, which is the scalar product of W and μ(z,u):
H(W,z,u) = W*t(z,u),(2.33)
Equation (2.34) is called the basic equation of the Pontryagin maximum principle, based on the dynamic programming equation. The optimal control is the one that delivers the Hamiltonian maximum over a given time interval. If the control resource were not limited, then necessary and sufficient extremum conditions could be used to determine optimal control. In a real situation, to find optimal control, it is necessary to analyze the value of the Hamiltonian at the limiting value of the level. In this case, U 0 will be a function of the extended state vector and the vector of conjugate coordinates u 0 = u 0 .
To find conjugate coordinates, it is necessary to solve the system of equations:
2.4.1 Procedure for calculating the system using the Pontryagin maximum principle.
The equations of the object must be brought to the standard form for the synthesis of optimal systems:
It is also necessary to specify the initial and final states and write down the optimality criterion.
The extended state vector is introduced
Extended vector of right sides:
And the vector of conjugate coordinates:
We write the Hamiltonian as a scalar product:
Finding the maximum of the Hamiltonian in u:
By which we determine the optimal control u 0 (Ш,z).
We write differential equations for the vector of conjugate coordinates:
We find the conjugate coordinates as a function of time:
6. Determine the final optimal control law:
As a rule, this method allows you to obtain a program control law.
Example 2.3 - For the object shown in Fig. 2. 9. it is necessary to ensure the transition from the starting point y(t) to the final point y(t) in T= 1c with the quality of the process:
Rice. 2.9 - Object model:
To determine the constants b 1 and b 2, it is necessary to solve a boundary value problem.
Let us write the equation of the closed system
Let's integrate:
Consider the end point t=T=1s., as x 1 (T)=1 and x 2 (T)=0:
1= 1/6 b 1 + 1/2 b 2
We obtained a system of equations from which we find b 2 = 6, b 1 = -12.
Let's write the control law u 0 = -12t + 6.
2.4.2 Optimal control problem
For a general object, it is necessary to ensure a transition from the starting point to the final point in the minimum time under a limited control law.
Features of the optimal performance problem
Performance Hamiltonian:
Control relay:
This feature occurs for relay objects.
Theorem on the number of switchings of the control action:
This theorem is valid for linear models with real roots of the characteristic equation.
Det (pI - A) =0 (2.51)
A(A) is a vector of real eigenvalues.
Statement of the theorem:
In the problem of optimal performance with real roots of the characteristic equation, the number of switchings cannot be greater than (n-1), where n is the order of the object, therefore, the number of control constancy intervals will not be greater than (n-1).
Rice. 2.10 - Type of control action at n=3:
Example 2.4 - Consider an example of solving the optimal performance problem:
Ш=[Ш 1 , Ш 2 ]
H b = W 1 x 2 + W 2 (-2dx 2 -x 1 +u)
When - the roots are real:
The sum of two exponents is:
If, then the roots are complex conjugate and the solution will be a periodic function. In a real system, there are no more than 5 - 6 switchings.
2.4.3 Switching surface method
This method allows you to find the control of state variable functions for the case when the optimal control is of a relay nature. Thus, this method can be used when solving problems of optimal performance for an object with additive control
The essence of the method is to identify points in the entire state space where the control sign changes and combine them into a common switching surface.
Switching surface
The control law will have the following form:
To form a switching surface, it is more convenient to consider the transition from an arbitrary starting point to the origin of coordinates
If the end point does not coincide with the origin, then it is necessary to select new variables for which this condition will be true.
We have an object of the form
We consider the transition, with the optimality criterion:
This criterion allows us to find a control law of this type:
With the unknown, the initial conditions are also unknown to us.
Considering the transition:
Reverse time method (reverse motion method).
This method allows you to determine the switching surfaces.
The essence of the method is that the starting and ending points are swapped, and instead of two sets of initial conditions, one remains for.
Each of these trajectories will be optimal. First, we find the points where the control changes sign and combine them into a surface, and then we change the direction of movement to the opposite.
Example - The transfer function of an object has the form:
Criterion for optimal performance:
Control restrictions.
Consider the transition:
Optimal control will have a relay nature:
Let's go to reverse time (i.e.). In reverse time the problem will look like this
Let's consider two cases:
We obtain the equations of the closed system:
Let us use the method of direct integration, we obtain a dependence on and since -, then we have
Because the starting and ending points are swapped, then we get similarly:
Let's build the result and use the phase plane method to determine the direction
Applying the direct integration method, we obtain:
The function will look like:
Changing direction:
Sign change point (switching point).
General analytical expression:
Surface equation:
Optimal control law:
Substituting the surface equation, we get:
2.5 Suboptimal systems
Suboptimal systems are systems that are close in properties to optimal ones.
Characterized by an optimality criterion.
Absolute error.
Relative error.
Suboptimal is a process that is close to optimal with a given accuracy.
A suboptimal system is a system where there is at least one suboptimal process.
Suboptimal systems are obtained in the following cases:
when approximating the switching surface (using piecewise linear approximation, approximation using splines)
When in a suboptimal system an optimal process will arise.
limiting the working area of the state space;
3. ADAPTIVE SYSTEMS
3.1 Basic concepts
Adaptive systems are those systems in which the controller parameters change following changes in the object parameters, so that the behavior of the system as a whole remains unchanged and corresponds to the desired:
There are two directions in the theory of adaptive systems:
adaptive systems with a reference model (ASEM);
adaptive systems with identifier (ASI).
3.2 Adaptive systems with ID
Identifier is a device for estimating object parameters (parameter assessment must be carried out in real time).
AR - adaptive regulator
OU - control object
U - identifier
The part that is highlighted with a dotted line can be implemented digitally:
V, U, X - can be vectors. An object can be multi-channel.
Let's consider the operation of the system.
In the case of unchanged object parameters, the structure and parameters of the adaptive controller do not change, the main Feedback, the system is a stabilization system.
If the parameters of the object change, then they are assessed by the identifier in real time and the structure and parameters of the adaptive controller change so that the behavior of the system remains unchanged. The main requirements are for the identifier (performance, etc.) and for the identification algorithm itself. This class of systems is used to control objects with slow nonstationarities. If we have a non-stationary object of general form:
;.The simplest adaptive view will be as follows:
Requirements for the system:
Where and are matrices of constant coefficients.
In reality we have:
If we equate, we obtain a relation for determining the controller parameters
3.3 Adaptive systems with reference model
In such systems, there is a reference model (EM), which is placed parallel to the object. BA - adaptation block.
Fig 2 - Functional diagram of ASEM:
Let's look at how the system works:
In the case when the object parameters do not change or the output processes correspond to the reference ones, the error is:
autotuning control programming
The adaptation unit does not work and the adaptive regulator is not rebuilt; smooth feedback operates in the system.
If the behavior is different from the reference one, this happens when changing the object's parameters, in which case an error appears.
The adaptation block is turned on, the structure of the adaptive controller is rebuilt in such a way as to reduce it to a reference model of the object.
The adaptation block must reduce the error to zero ().
The algorithm included in the adaptation block is formed different ways, for example, using the second Lyapunov method:
If this is true, then the system will be asymptotically stable and.
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The optimization problem usually consists of finding and maintaining such control actions that ensure the extremum of a certain criterion for the quality of functioning of the control object. This problem can be solved automatically with the help of extreme controllers, which search for optimal control actions during operation. Systems that implement automatic search and tracking of the extremum of a certain indicator of the quality of an object’s operation are called extreme control systems or automatic optimization systems. Automatic optimization systems, thanks to the implementation of algorithms for searching for optimal controls, have a number of advantages, the main one of which is their ability to function normally in conditions of incomplete a priori information about the object and about the disturbances acting on it. The use of extreme control systems is advisable in cases where the criterion for the quality of an object’s operation has a pronounced extremum and there are opportunities to search for and maintain its optimal (extreme) mode of operation. The development of the theory and technology of extreme control systems has now reached a significant level. The industry produces standard extreme regulators (automatic optimizers) for a number of technological processes.
Extreme control systems constitute one of the most theoretically and practically developed classes of adaptive systems. Extreme are those automatic control objects in which the static characteristic has an extremum, the position and value of which are unknown and can change continuously.
Typically, an extremal controller searches for and maintains such values of object coordinates at which the output 
reaches extreme values. This mode of operation of the object and the system as a whole is optimal in the sense of a minimum or maximum quality criterion. An example of a one-dimensional extreme object is an airplane. Dependence of kilometer fuel consumption y on flight speed x characterized by the presence of an extremum, the magnitude and position of which change as the weight of the aircraft changes due to fuel consumption.
Depending on the number of extrema, objects are divided into single-extremal and multi-extremal, and in the latter case, the control task is to find a global extremum, i.e. the greatest maximum or the smallest minimum. Depending on the number of control actions generated in the extreme controller, one-dimensional and multidimensional extreme control systems are distinguished. By the nature of their work over time, extreme systems can be continuous or discrete. Depending on the nature of the search signal, extreme systems with deterministic and random search signals are distinguished.
Goal of the work
To become familiar with the construction of step-by-step extremal control systems for controlling dynamic objects with delay.
Theoretical part
In any production (factory, plant) there is a certain leading technical and economic indicator (TEI), which fully characterizes the efficiency of this production. It is beneficial to maintain this leading indicator at an extreme value. Such a general indicator may be the profit of the enterprise.
For all technological processes (in workshops, departments) that are part of production, based on the leading TEP, you can formulate your own private TEP (for example, the cost of a unit of production at a given productivity). In turn, the technological process can usually be divided into a number of sections (technological units), for each of which it is also possible to find an optimality criterion Q . Reaching the extremum Q will bring the private TEP of the process and the leading TEP of production as a whole closer to the extremum.
Optimality criterion Q can be directly any technological parameter (for example, the temperature of the combustion device torch) or some function depending on the technological parameters (for example, efficiency, thermal effect of the reaction, yield of useful product for a given period of time, etc. ).
If the optimality criterion Q is a function of some object parameters, then an extreme control system (ERS) can be used to optimize this object.
In the general case, the value of the optimality criterion depends on changes in a number of input parameters of the object. There are many control objects for which the value of the optimality criterion Q depends mainly on changing one input parameter. Examples of such objects include various types of combustion devices, catalytic reactors, chemical water treatment at thermal power plants and many others.
So, extreme control systems are designed to search for optimal values of control actions, i.e. such values that provide the extremum of some criterion Q optimality of the process.
Extreme control systems, which are designed to optimize an object using one input channel, are called single-channel. Such SERs are most widespread.
When optimizing objects with significant inertia and pure delay, it is advisable to use step extremal systems that act on the controlled input of the object at discrete time intervals.
When studying an extremal system, in most cases it is convenient to represent the optimization object as a series connection of three links: an input linear inertial link, an extreme static characteristic at = F(X) and the output linear inertial link (Fig. 1). This structural substitution scheme can be designated LNL.
Rice. 1Scheme of an extreme LNL object
It is convenient to take the gain factors of both linear links equal to one. If the inertia of the input linear link is negligible compared to the inertia of the output linear link, the object can be represented by an NL equivalent circuit; if the inertia of the output linear link is negligible, use an LN equivalent circuit. The intrinsic inertial properties of an object are usually represented by an output inertial link; The inertia of the system’s measuring devices belongs to the same link.
An input linear link usually appears in the structural diagram of an object when the actuator mechanism (AM) of an extreme system acts on the optimization object itself through a link that has inertia, for example, if the input parameter of the object being optimized is temperature, and the AM influences its change through the heat exchanger. The inertia of the actuator is also included in the input linear part.
It should be noted that the coordinates of the control object intermediate between linear and nonlinear links in the vast majority of cases cannot be measured; this is easy to do only by modeling the system.
In some cases, it is possible to determine the structural diagram of the replacement of an object only experimentally.
To do this, you should change the input coordinate of the object v 1, corresponding to the output value z 1 , before v 2 (Fig. 2, A), at which the value of the output coordinate of the object as a result of the transient process will be approximately equal to z 1 .
If this disturbance practically did not cause any noticeable change in the output coordinate of the object (Fig. 2, b), then there is no input inertial link. If the transition process as a result of such a disturbance has a form qualitatively close to that shown in Fig. 2, V, then the inertial link at the input of the object exists.
Rice. 2Characteristics of an extreme op-amp
The structure of NL and LL objects, in which the linear part is described by a first-order differential equation with or without delay, and the static characteristic y=f(x) can be any continuous function with one extremum in the operating range can be approximated sufficiently a large number of industrial facilities optimization.
Extreme Control Systems:
Automatic optimization systems with extremum memorization
In extreme SAO controllers with extremum memorization, the difference between the current value of the output signal is supplied to the signal relay at object and its value at the previous point in time.
The block diagram of the self-regulatory system with extremum memorization is shown in Fig. 3 . Output value of the object ABOUT with static characteristic y=f(X) fed to the storage device memory extreme regulator.
Rice. 3Automatic optimization system with extremum memorization
The storage device of such a system should only record an increase in the input signal, i.e. memorization occurs only with increasing u. To decrease at The storage device does not respond. The signal from the storage device is continuously supplied to the comparison element ES, where it is compared with the current signal value u. Difference signal at-at max from the comparison element goes to the signal relay SR. When the difference at-y max reaches the deadband value u n signal relay, it reverses the actuator THEM, which affects the input signal X object. After activation, the signal relay is stored by the memory device memory meaning y the signal is reset and stored at starts again.
Systems with extremum memory usually have actuators with a constant speed of movement, i.e. dx/dt=±k 1 Where k=const. Depending on the signal And The signal relay actuator changes the direction of movement.
Let us explain the operation of the SAO with memorization of the extremum. Let us assume that at the moment t 1 (Fig. 4), when the state of the object is characterized by the values of the signals at the input and output, respectively X 1 And at 1 (dot M 1), The extreme regulator is turned on. At this moment, the memory device stores the signal at 1 . Let’s assume that the extreme controller, after being put into operation, began to increase the value X, in this case the value at decreases - the storage device does not respond to this. As a result, a signal appears at the output of the signal relay at-at 1 . In the moment t signal at-at 1 reaches the dead zone of the signal relay u n(dot M 2), which is triggered, reversing the actuator. After this, the stored value at 1 is reset and the storage device stores the new value at 2 . Object input signal X decreases, and the output signal at increases (trajectory from point M 2 To M 3). Because the at increasing all the time, output memory continuously follows change u.
Rice. 4Search for the optimum in the SAO with memorization of the extremum:
A- characteristics of the object; b- changing the output of the object; V- signal at the input of the signal relay; G- changing the object's input.
At the point M 3 the system reaches an extremum, but the decrease X continues. As a result, after the point M 3 meaning at is already decreasing and memory remembers y Max. Now there is a signal relay at the input SR the difference signal appears again y-y max. At the point M 4 , When y 4 -y max = y n, the signal relay is triggered, reversing the actuator and resetting the stored value y max, etc.
Oscillations are established around the extremum of the controlled value. From Fig. 4 it can be seen that the period of input oscillations T in object is 2 times longer than the oscillation period of the object's output T out. The signal relay reverses the MI when y=y max - y n. The direction of movement of the IM after the activation of the signal relay depends on the direction of movement of the IM before the operation of the signal relay.
From the examination of the operation of the SAO with extremum memorization, it is clear that its name does not accurately reflect the essence of the system’s operation. The storage device does not record the extremum of the static characteristic of the object (its value at the moment the controller is turned on is unknown). The memory device records the values of the output quantity at object when at increases.
Step-type automatic optimization systems
The block diagram of a step-by-step automatic control system is shown in Fig. 5. Output signal measurement at object in the system occurs discretely (behind the object output sensor there is a pulse element IE 1), i.e. at certain intervals ∆ t(∆t- repetition period of the pulse element). Thus, the pulse element converts the changing output signal at object into a sequence of pulses, the height of which is proportional to the values at at moments in time t=n∆t, called removal moments. Let's denote the values at at a point in time t=n∆t through at p. Values y n are fed to the memory storage device (delay element). The storage device supplies the comparison element ES previous value u p- 1 . On ES simultaneously arrives y n. The output of the comparison element produces a difference signal ∆y n =y n - u p- 1 IN next moment t=(n+1) ∆t signal pickup stored value u p- 1 is reset from the memory and the signal is stored y n+ 1 , a signal y n comes from memory on ES and at the input signal relay SR signal ∆ appears y n+ 1 = y n + 1 -y n .
Rice. 5Discrete structure(stepper)SAO
So, a signal proportional to the increment ∆ is supplied to the signal relay in the stepper automatic control system at output of an object over a period of time ∆ t. If ∆ y>0 then such movement is allowed by the signal relay; if ∆ at<0, then the signal relay is activated and changes the direction of the input signal X.
Between signal relay SR and actuator THEM(Fig. 5) another pulse element is included IE 2 (working in sync with IE 1), which periodically opens the power circuit THEM, stopping THEM for this time.
The actuator in such automated systems usually changes the input X object in steps to a constant value ∆х. It is advisable to quickly change the input signal of the object by step so that the time it takes to move the actuator by one step is sufficiently short. In this case, the disturbances introduced into the object by the actuator will approach abrupt ones.
Thus, the signal relay changes the direction of the subsequent step ∆ x n+ 1 actuator, if the value ∆ y n becomes less than zero.
Let us consider the nature of the search for an extremum in a step-by-step automated system with an inertia-free object. Let us assume that the initial state of the object is characterized by point M 1 on the static dependence y=f(x) (Fig. 6, a). Let us assume that the extreme controller comes into operation at the moment of time t 1 and the actuator takes a step ∆ X to increase the object input signal.
Rice. 6Search in discrete CAO: A - characteristics of the object; b- change in output; V- input change
Object output signal at at the same time it also increases. After time ∆ t(at time t 2) the actuator takes a step in the same direction, since ∆ at 1 =y 2 -y 1 >0. In the moment t 3 the actuator makes another step by ∆ X in the same direction, since ∆ y 2 =y 3 -y 2 is greater than zero, etc. At the moment of time t 5 increment of the object output signal ∆ y 3 =y 5 -y 4 , becomes less than zero, the signal relay is triggered and the next step ∆ X the actuator will make in the direction of decreasing the object input signal X etc.
In step-by-step automatic control systems, to ensure stability, it is necessary that the movement of the system towards the extremum be non-monotonic.
There are step-by-step CAOs, at which change the input signal in one step ∆ X variable and depends on the value y.
Automatic optimization systems with derivative control
Automatic optimization systems with derivative control use the property of the extreme static characteristic that the derivative dy/dx equal to zero at the value of the object input signal x=x wholesale(see Fig. 7).
Rice. 7Graph of changes in the derivative of a unimodal characteristic
The block diagram of one of these self-propelled systems is shown in Fig. 8. The values of the input and output signals of the object O are fed to two differentiators D 1 And D 2 , at the output of which signals are obtained, respectively dx/dt And dy/dt. Derivative signals are sent to the dividing device DU.
Rice. 8Structure of the self-regulatory system with measurement of the derivative of the static characteristic
At the exit DU a signal is obtained dy/dx, which is fed to the amplifier U with gain k 2. The signal from the amplifier output goes to the actuator THEM with variable speed of movement, the value of which is proportional to the output signal of the amplifier And. Gain THEM equals k 1 .
If the static characteristic of the object y=f(x) has the shape of a parabola y=-kx 2 , then the SAO is described by linear equations (in the absence of disturbances), since dy/dx=-2kx, and the remaining links of the system are linear. A logical device for determining the direction of movement towards the extremum is not used in such a system, since it is purely linear and in it, it would seem, the value of the extremum is known in advance (since dy/dx= 0 at x=x oiit).
At the moment the self-propelled gun system is put into operation at THEM some signal is given to set it in motion, otherwise dx/dt= 0 And dy/dt= 0 (in the absence of random disturbances). After this, the ACS works like a regular ACS, whose task is the value dy/dx= 0.
The described system has a number of disadvantages that make it practically unusable. Firstly, when dx/dt→ 0 derivative dy/dt also tends to zero - the task of finding the extremum becomes uncertain. Secondly, real objects have a delay, so it is necessary to divide derivatives that are not simultaneously measured by each other dy/dt And dx/dt and shifted in time exactly by the delay time of the signal in the object, which is quite difficult to accomplish. Thirdly, the absence of a logical device (signal relay) in such an automated system leads to the fact that in some conditions the system loses its functionality. Let’s assume that the self-propelled gun started working when x
In addition, even if such a system moves towards an extremum at the initial moment, it loses its functionality with an arbitrarily small drift of the static characteristic without a test reverse switch.
Rice. 9Optimization system with measurement of the derivative of the object's output:
A - system structure; b- characteristics of the object; V- change in output; G- input signal, d - changing the object's input.
Let's consider another type of self-regulatory system with derivative measurement and an actuator THEM constant speed of movement, the block diagram of which is shown in Fig. 9.
Let us consider the nature of the search for the SAO extremum with the measurement of the derivative with the block diagram shown in Fig. 9, A.
Let the inertialess object of regulation ABOUT(Fig. 9, a) has a static characteristic shown in Fig. 9, b. The state of the automatic control system at the moment the extreme controller is turned on is determined by the values of the input signals x 1 and exit at 1 - dot M 1 on the static characteristic.
Let us assume that the extremal controller, after being put into operation at the moment of time t 1 changes the input signal X in the direction of increase. In this case, the signal at the output of the object at will change in accordance with the static characteristic (Fig. 9, V), and the derivative dy/dt when moving from a point M 1 before M 2 decreases (Fig. 9, G). At a moment in time t 2 the object's output will reach an extremum at max, and the derivative dy/dt will be equal to zero. Due to the insensitivity of the signal relay, the system will continue to move, moving away from the extremum. In this case, the derivative dy/dt will change sign and become negative. In the moment t 3 , when the value dy/dt remaining negative, it will exceed the dead zone of the signal relay ( dy/dt)H, the actuator will reverse and the input signal will X will begin to decrease. The output of the object will begin to approach the extremum again, and the derivative dy/dt will become positive when moving from the point M 3 before M 4 (Fig. 9, V). At a moment in time t 4, the output signal again reaches an extremum, and the derivative dy/dt=0.
However, due to the insensitivity of the signal relay, the movement of the system will continue, the derivative dy/dt will become negative at the point M 5 reverse will occur again, etc.
In this system, only the output signal of the object is differentiated, which is fed to the signal relay SR. Since when the system passes through an extremum the sign dy/dt changes, then to find the extremum you need to reverse THEM, when derivative dy/dt will become negative and exceed the dead zone ( dy/dt)H Signum relay.
Sign responsive system dy/dt according to the principle of operation it is close to the step-by-step self-propelled gun, but is less noise-resistant.
Automatic optimization systems with auxiliary modulation
In some works, such automatic optimization systems are called systems with a continuous search signal or, in the terminology of A.A. Krasovsky simply by continuous systems of extreme regulation.
These systems use the property of the static characteristic to change the phase of oscillations of the object's output signal compared to the phase of the object's input oscillations by 180° when the object's output signal passes through an extremum (see Fig. 10).
Rice. 10The nature of the passage of harmonic vibrations through the unimodal characteristic
Unlike the SAO systems discussed above, systems with auxiliary modulation have separate search and working movements.
The block diagram of the self-regulatory system with auxiliary modulation is shown in Fig. 11. Input signal X object O with characteristic y=f(x) is the sum of two components: x=xo(t)+a sin ω 0 t, Where A And ω 0 - constant values. Component a sin ω 0 t is a test movement and is generated by a generator G, component x o(t) is a labor movement. When moving towards an extremum, the variable component a sin ω 0 t input signal of the object causes the appearance of an alternating component of the same frequency ω 0 =2π/T 0 in the output signal of the object (see Fig. 10). The variable component can be found graphically, as shown in Fig. 10.
Rice. elevenSAO structure with auxiliary modulation
It is obvious that the variable component of the signal at the output of the object is in phase with the variable component of the signal at the input for any input value, when x 0 =x 1
Amplitude A search oscillations should be small, since these oscillations pass into the output signal of the object and lead to an error in determining the extremum.
Component of the quantity y, having a frequency ω 0, isolated by bandpass filter F 1 (Fig. 11). Filter task F 1 is not to miss the constant or slowly changing component and the components of the second and higher harmonics. Ideally, the filter should pass only the component with frequency ω 0.
After filter F 1 variable component of quantity y, having a frequency ω 0, supplied to the multiplying link MOH(synchronous detector). The reference value is also supplied to the input of the multiplier link v 1 =a sin ( ω 0 t + φ ). Phase φ reference voltage v 1 selected depending on the phase of the filter output F 1 , since filter Ф 1 introduces an additional phase shift.
Voltage at the output of the multiplier section u=vv 1 . When value x<x wholesale
u = vv 1 = b sin ( ω 0 t+ φ ) a sin ( ω 0 t+ φ ) = ab sin 2 ( ω 0 t + φ )= = ab/ 2 .
When the input signal value x>X 0PT signal value at the output of the multiplier section MOH is:
u = vv 1 = b sin ( ω 0 t + φ + 180°) a sin ( ω 0 t + φ ) = - ab sin 2 ( ω 0 t + φ )= = - ab/ 2 .
Rice. 12The nature of the search in SAO with auxiliary modulation:
A - characteristics of the object; b-change in oscillation phase; V- harmonic oscillations at the input; G- total signal at the input; d - signal at the output of the multiplier section.
After the multiplying section the signal And fed to a low-pass filter F 2, which does not pass the variable component of the signal And. DC signal component and=and 1 after the filter F 2 is supplied to the relay element RE. The relay element controls the actuator at a constant speed. Instead of a relay element, the circuit may have a phase-sensitive amplifier; then the actuator will have a variable speed of movement.
In Fig. Figure 12 shows the nature of the search for an extremum in the SAO with auxiliary modulation, the block diagram of which is shown in Fig. 11. Suppose that the initial state of the system is characterized by signals at the input and output of the object, respectively X 1 And y 1 (dot M 1 in Fig. 12, a).
Because at the point M 1 meaning x 1 <х опт then when the extreme regulator is turned on, the phases of the input and output oscillations will coincide. Let us assume that the constant component at the filter output is F 2 positive ( ab/2>0), which corresponds to movement with increasing X, i.e. dx 0 /dt>0. In this case, the SAO will move towards the extremum.
If the starting point M 2, which characterizes the position of the system at the moment the extreme controller is turned on, is such that the object input signal x>x opt (Fig. 12, a), then the oscillations of the input and output signals of the object are in antiphase. As a result, the constant component at the output F 2 will be negative ( ab/2<0), что вызовет движение системы в сторону уменьшения X (dx 0 /dt<0 ). In this case, the SAO will approach the extremum.
Thus, regardless of the initial state of the system, the search for an extremum will be ensured.
In systems with a variable speed actuator, the speed of movement of the system to the extremum will depend on the amplitude of the output oscillations of the object, and this amplitude is determined by the deviation of the input signal X from the value X wholesale
