Optimal Control of Dynamic Systems Driven by Vector Measures, Ahmed

Автор: Brunton, Steven L. (university Of Washington) Kutz
Название: Data-driven science and engineering
ISBN: 1009098489 ISBN-13(EAN): 9781009098489
Издательство: Cambridge Academ
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Цена: 52790.00 T
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Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.

Автор: Shengbo Eben Li
Название: Reinforcement Learning for Sequential Decision and Optimal Control
ISBN: 9811977836 ISBN-13(EAN): 9789811977831
Издательство: Springer
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Цена: 74530.00 T
Наличие на складе: Нет в наличии.
Описание: Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman’s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on.

Автор: Fu
Название: Dynamic Optimization of Path-Constrained Switched Systems
ISBN: 3031234278 ISBN-13(EAN): 9783031234279
Издательство: Springer
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Цена: 130430.00 T
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Описание: This book provides a series of systematic theoretical results and numerical solution algorithms for dynamic optimization problems of switched systems within infinite-dimensional inequality path constraints. Dynamic optimization of path-constrained switched systems is a challenging task due to the complexity from seeking the best combinatorial optimization among the system input, switch times and switching sequences. Meanwhile, to ensure safety and guarantee product quality, path constraints are required to be rigorously satisfied (i.e., at an infinite number of time points) within a finite number of iterations. Several novel methodologies are presented by using dynamic optimization and semi-infinite programming techniques. The core advantages of our new approaches lie in two folds: i) The system input, switch times and the switching sequence can be optimized simultaneously. ii) The proposed algorithms terminate within finite iterations while coming with a certification of feasibility for the path constraints. In this book, first, we provide brief surveys on dynamic optimization of path-constrained systems and switched systems. For switched systems with a fixed switching sequence, we propose a bi-level algorithm, in which the input is optimized at the inner level, and the switch times are updated at the outer level by using the gradient information of the optimal value function calculated at the optimal input. We then propose an efficient single-level algorithm by optimizing the input and switch times simultaneously, which greatly reduces the number of nonlinear programs and the computational burden. For switched systems with free switching sequences, we propose a solution framework for dynamic optimization of path-constrained switched systems by employing the variant 2 of generalized Benders decomposition technique. In this framework, we adopt two different system formulations in the primal and master problem construction and explicitly characterize the switching sequences by introducing a binary variable. Finally, we propose a multi-objective dynamic optimization algorithm for locating approximated local Pareto solutions and quantitatively analyze the approximation optimality of the obtained solutions. This book provides a unified framework of dynamic optimization of path-constrained switched systems. It can therefore serve as a useful book for researchers and graduate students who are interested in knowing the state of the art of dynamic optimization of switched systems, as well as recent advances in path-constrained optimization problems. It is a useful source of up-to-date optimization methods and algorithms for researchers who study switched systems and graduate students of control theory and control engineering. In addition, it is also a useful source for engineers who work in the control and optimization fields such as robotics, chemical engineering and industrial processes.

Автор: Jinna Li , Frank L. Lewis , Jialu Fan
Название: Reinforcement Learning : Optimal Feedback Control with Industrial Applications.
ISBN: 3031283937 ISBN-13(EAN): 9783031283932
Издательство: Springer
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Цена: 130430.00 T
Наличие на складе: Нет в наличии.
Описание: This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.

Автор: Liu
Название: Dynamic Modeling and Boundary Control of Flexible Axially Moving System
ISBN: 981196940X ISBN-13(EAN): 9789811969409
Издательство: Springer
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Цена: 121110.00 T
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Описание: The main objectives of the book are to introduce the design method of boundary control strategies for the axially moving structures to reduce their vibration. This book provides the reader with a thorough grounding in the boundary controller design. Our goal is to provide advanced boundary controller design methods and their stability analysis methods and offer simulation examples and MATLAB programs for each boundary control algorithm. For each chapter, several engineering application examples are given and the contents of each chapter in this book are independent, so that readers can just read their own needs. In this book, all the control algorithms and their programs are described separately and classified by the chapter name, which can be run successfully in MATLAB. The book can benefit researchers, engineers, and graduate students in the fields of PDE modeling and boundary vibration control of flexible structures.

Автор: Peter Stefanidis; Andrzej P. Paplinski; Michael J.
Название: Numerical Operations with Polynomial Matrices
ISBN: 3540549927 ISBN-13(EAN): 9783540549925
Издательство: Springer
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Цена: 104480.00 T
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Описание: The purpose of this monograph is to describe a class of com-putational methods, based on polynomial matrices, for thedesign of dynamic compensators for linear multi-variablecontrol systems.

Автор: Gang Tao; Frank L. Lewis
Название: Adaptive Control of Nonsmooth Dynamic Systems
ISBN: 1849968691 ISBN-13(EAN): 9781849968690
Издательство: Springer
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Цена: 174130.00 T
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Описание: Many of the non-smooth, non-linear phenomena covered in this well-balanced book are of vital importance in almost any field of engineering. Contributors from all over the world ensure that no one area`s slant on the subjects predominates.

Автор: Sun Jingrui, Yong Jiongmin
Название: Stochastic Linear-Quadratic Optimal Control Theory: Open-Loop and Closed-Loop Solutions
ISBN: 3030209210 ISBN-13(EAN): 9783030209216
Издательство: Springer
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Цена: 60550.00 T
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Описание: This book gathers the most essential results, including recent ones, on linear-quadratic optimal control problems, which represent an important aspect of stochastic control.

Автор: Karl-Heinz Hoffmann; Werner Krabs
Название: Optimal Control of Partial Differential Equations
ISBN: 3540535918 ISBN-13(EAN): 9783540535911
Издательство: Springer
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Цена: 81050.00 T
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Описание: The purpose of the Conference on Optimal Control of Partial Differential Equations was to bring together leading experts in this field and to exchange ideas and information about recent advances in control theory connected with partial differential equations.

Автор: Aziz Belmiloudi
Название: Stabilization, Optimal and Robust Control
ISBN: 1849967903 ISBN-13(EAN): 9781849967907
Издательство: Springer
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Цена: 278580.00 T
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Описание: The material here develops the robust control of infinite-dimensional dynamical systems derived from time-dependent coupled PDEs associated with boundary-value problems. Mathematical foundations are provided to keep the book accessible to the non-specialist.

Автор: Alexander Zaslavski
Название: Turnpike Properties in the Calculus of Variations and Optimal Control
ISBN: 1441939245 ISBN-13(EAN): 9781441939241
Издательство: Springer
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Цена: 135090.00 T
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Описание: Infinite Horizon Variational Problems.- Extremals of Nonautonomous Problems.- Extremals of Autonomous Problems.- Infinite Horizon Autonomous Problems.- Turnpike for Autonomous Problems.- Linear Periodic Control Systems.- Linear Systems with Nonperiodic Integrands.- Discrete-Time Control Systems.- Control Problems in Hilbert Spaces.- A Class of Differential Inclusions.- Convex Processes.- A Dynamic Zero-Sum Game.

Автор: Vui Ha Huy Et Al
Название: Genericity In Polynomial Optimization
ISBN: 1786342219 ISBN-13(EAN): 9781786342218
Издательство: World Scientific Publishing
Цена: 77090.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

In full generality, minimizing a polynomial function over a closed semi-algebraic set requires complex mathematical equations. This book explains recent developments from singularity theory and semi-algebraic geometry for studying polynomial optimization problems. Classes of generic problems are defined in a simple and elegant manner by using only the two basic (and relatively simple) notions of Newton polyhedron and non-degeneracy conditions associated with a given polynomial optimization problem. These conditions are well known in singularity theory, however, they are rarely considered within the optimization community.

Explanations focus on critical points and tangencies of polynomial optimization, HOlderian error bounds for polynomial systems, Frank-Wolfe-type theorem for polynomial programs and well-posedness in polynomial optimization. It then goes on to look at optimization for the different types of polynomials. Through this text graduate students, PhD students and researchers of mathematics will be provided with the knowledge necessary to use semi-algebraic geometry in optimization.