Discrete Time Inverse Optimal Control For Nonlinear Systems
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Discrete Time Inverse Optimal Control For Nonlinear Systems
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Author : Edgar N. Sanchez
language : en
Publisher: CRC Press
Release Date : 2017-12-19
Discrete Time Inverse Optimal Control For Nonlinear Systems written by Edgar N. Sanchez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-19 with Technology & Engineering categories.
Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Learn from Simulations and an In-Depth Case Study The authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels. The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control.
Discrete Time Inverse Optimal Control For Nonlinear Systems
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Author : Edgar Sanchez
language : en
Publisher:
Release Date : 2016
Discrete Time Inverse Optimal Control For Nonlinear Systems written by Edgar Sanchez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Mathematics categories.
Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Learn from Simulations and an In-Depth Case Study The authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels. The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control.
Self Learning Optimal Control Of Nonlinear Systems
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Author : Qinglai Wei
language : en
Publisher: Springer
Release Date : 2017-06-13
Self Learning Optimal Control Of Nonlinear Systems written by Qinglai Wei and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-13 with Technology & Engineering categories.
This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.
Integral And Inverse Reinforcement Learning For Optimal Control Systems And Games
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Author : Bosen Lian
language : en
Publisher: Springer Nature
Release Date : 2024-03-05
Integral And Inverse Reinforcement Learning For Optimal Control Systems And Games written by Bosen Lian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-05 with Technology & Engineering categories.
Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverse optimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains – aircraft, robotics, power systems, and communication networks among them – with theoretical insights valuable in tackling the real-world challenges they face.
Inverse Optimal Control And Inverse Noncooperative Dynamic Game Theory
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Author : Timothy L. Molloy
language : en
Publisher: Springer Nature
Release Date : 2022-02-18
Inverse Optimal Control And Inverse Noncooperative Dynamic Game Theory written by Timothy L. Molloy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-18 with Mathematics categories.
This book presents a novel unified treatment of inverse problems in optimal control and noncooperative dynamic game theory. It provides readers with fundamental tools for the development of practical algorithms to solve inverse problems in control, robotics, biology, and economics. The treatment involves the application of Pontryagin's minimum principle to a variety of inverse problems and proposes algorithms founded on the elegance of dynamic optimization theory. There is a balanced emphasis between fundamental theoretical questions and practical matters. The text begins by providing an introduction and background to its topics. It then discusses discrete-time and continuous-time inverse optimal control. The focus moves on to differential and dynamic games and the book is completed by consideration of relevant applications. The algorithms and theoretical results developed in Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory provide new insights into information requirements for solving inverse problems, including the structure, quantity, and types of state and control data. These insights have significant practical consequences in the design of technologies seeking to exploit inverse techniques such as collaborative robots, driver-assistance technologies, and autonomous systems. The book will therefore be of interest to researchers, engineers, and postgraduate students in several disciplines within the area of control and robotics.
Nonlinear Dynamical Systems And Control
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Author : Wassim M. Haddad
language : en
Publisher: Princeton University Press
Release Date : 2011-09-19
Nonlinear Dynamical Systems And Control written by Wassim M. Haddad and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-19 with Mathematics categories.
Nonlinear Dynamical Systems and Control presents and develops an extensive treatment of stability analysis and control design of nonlinear dynamical systems, with an emphasis on Lyapunov-based methods. Dynamical system theory lies at the heart of mathematical sciences and engineering. The application of dynamical systems has crossed interdisciplinary boundaries from chemistry to biochemistry to chemical kinetics, from medicine to biology to population genetics, from economics to sociology to psychology, and from physics to mechanics to engineering. The increasingly complex nature of engineering systems requiring feedback control to obtain a desired system behavior also gives rise to dynamical systems. Wassim Haddad and VijaySekhar Chellaboina provide an exhaustive treatment of nonlinear systems theory and control using the highest standards of exposition and rigor. This graduate-level textbook goes well beyond standard treatments by developing Lyapunov stability theory, partial stability, boundedness, input-to-state stability, input-output stability, finite-time stability, semistability, stability of sets and periodic orbits, and stability theorems via vector Lyapunov functions. A complete and thorough treatment of dissipativity theory, absolute stability theory, stability of feedback systems, optimal control, disturbance rejection control, and robust control for nonlinear dynamical systems is also given. This book is an indispensable resource for applied mathematicians, dynamical systems theorists, control theorists, and engineers.
Discrete Time Recurrent Neural Control
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Author : Edgar N. Sanchez
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Discrete Time Recurrent Neural Control written by Edgar N. Sanchez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Technology & Engineering categories.
The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme. "This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems. The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market. It is an excellent book after all." — Guanrong Chen, City University of Hong Kong "This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author." — Alma Y. Alanis, University of Guadalajara, Mexico "This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones." — Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India
Feedback Control For Personalized Medicine
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Author : Esteban A. Hernandez-Vargas
language : en
Publisher: Academic Press
Release Date : 2022-04-21
Feedback Control For Personalized Medicine written by Esteban A. Hernandez-Vargas and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-21 with Science categories.
Feedback Control for Personalized Medicine provides ideas on ongoing efforts and obstacles by members of the control engineering community in different biological and medical applications. In addition, the book presents key challenges, insights, tools and theoretical developments that arise from personalized medicine, along with medical concepts that are explained by engineers to help non-experts follow research topics. Several clinical trials have tried to find therapeutic approaches to achieve eradication or at least lifelong, therapy-free, host control of the infection. This has been performed integrating clinical observations, empirical knowledge and information from medical tests to treat patients. As this "trial and error approach is becoming more challenging and unfeasible by the steep increase in the number of different pieces of information and the complexity of large datasets, a systematic and tractable approach that integrates a variety of biological and medical research data into mathematical models and computational algorithms is crucial to harness knowledge and to develop new therapies towards personalized medicine. - Presents the most recent research in personalized medicine using control theoretical tools - Offers numerical simulations that are analyzed in detail and compared with control experiments - Brings the most recent research of control theory in medicine
Doubly Fed Induction Generators
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Author : Edgar N. Sanchez
language : en
Publisher: CRC Press
Release Date : 2016-08-05
Doubly Fed Induction Generators written by Edgar N. Sanchez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-05 with Technology & Engineering categories.
Doubly Fed Induction Generators: Control for Wind Energy provides a detailed source of information on the modeling and design of controllers for the doubly fed induction generator (DFIG) used in wind energy applications. Focusing on the use of nonlinear control techniques, this book: Discusses the main features and advantages of the DFIG Describes key theoretical fundamentals and the DFIG mathematical model Develops controllers using inverse optimal control, sliding modes, and neural networks Devises an improvement to add robustness in the presence of parametric variations Details the results of real-time implementations All controllers presented in the book are tested in a laboratory prototype. Comparisons between the controllers are made by analyzing statistical measures applied to the control objectives.
Computational Intelligence And Industrial Applications
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Author : Bin Xin
language : en
Publisher: Springer Nature
Release Date : 2025-04-14
Computational Intelligence And Industrial Applications written by Bin Xin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Computers categories.
This two-volume set CCIS 2465-2466, constitutes of the proceedings of 11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024, held in Beijing, China, during November 1–5, 2024. The 55 full papers and 5 short papers included in this volume were carefully reviewed and selected from 135 submissions. The topics cover the following fields connected to computational intelligence and intelligent informatics: intelligent information processing, pattern recognition and computer vision, intelligent optimization and decision-making, advanced control, multi-agent systems, robotics and various applications of computational intelligence methods such as neural networks, fuzzy reasoning, evolutionary computing, machine learning and deep learning.