Self Learning Optimal Control Of Nonlinear Systems

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Self-Learning Optimal Control of Nonlinear Systems

Qinglai Wei,Ruizhuo Song,Benkai Li,Xiaofeng Lin
Self Learning Optimal Control of Nonlinear Systems Book PDF

Author : Qinglai Wei,Ruizhuo Song,Benkai Li,Xiaofeng Lin
Publisher : Springer
Page : 230 pages
File Size : 52,6 Mb
Release : 2017-06-13
Category : Technology & Engineering
ISBN : 9789811040801

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Self-Learning Optimal Control of Nonlinear Systems by Qinglai Wei,Ruizhuo Song,Benkai Li,Xiaofeng Lin Pdf

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.

Nonlinear and Optimal Control Systems

Thomas L. Vincent,Walter J. Grantham
Nonlinear and Optimal Control Systems Book PDF

Author : Thomas L. Vincent,Walter J. Grantham
Publisher : John Wiley & Sons
Page : 584 pages
File Size : 52,8 Mb
Release : 1997-06-23
Category : Science
ISBN : 0471042358

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Nonlinear and Optimal Control Systems by Thomas L. Vincent,Walter J. Grantham Pdf

Designed for one-semester introductory senior-or graduate-level course, the authors provide the student with an introduction of analysis techniques used in the design of nonlinear and optimal feedback control systems. There is special emphasis on the fundamental topics of stability, controllability, and optimality, and on the corresponding geometry associated with these topics. Each chapter contains several examples and a variety of exercises.

Deep Reinforcement Learning with Guaranteed Performance

Yinyan Zhang,Shuai Li,Xuefeng Zhou
Deep Reinforcement Learning with Guaranteed Performance Book PDF

Author : Yinyan Zhang,Shuai Li,Xuefeng Zhou
Publisher : Springer Nature
Page : 225 pages
File Size : 42,7 Mb
Release : 2019-11-09
Category : Technology & Engineering
ISBN : 9783030333843

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Deep Reinforcement Learning with Guaranteed Performance by Yinyan Zhang,Shuai Li,Xuefeng Zhou Pdf

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

Discrete-Time Inverse Optimal Control for Nonlinear Systems

Edgar N. Sanchez,Fernando Ornelas-Tellez
Discrete Time Inverse Optimal Control for Nonlinear Systems Book PDF

Author : Edgar N. Sanchez,Fernando Ornelas-Tellez
Publisher : CRC Press
Page : 268 pages
File Size : 54,8 Mb
Release : 2017-12-19
Category : Technology & Engineering
ISBN : 9781466580886

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Discrete-Time Inverse Optimal Control for Nonlinear Systems by Edgar N. Sanchez,Fernando Ornelas-Tellez Pdf

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.

Advanced Optimal Control and Applications Involving Critic Intelligence

Ding Wang,Mingming Ha,Mingming Zhao
Advanced Optimal Control and Applications Involving Critic Intelligence Book PDF

Author : Ding Wang,Mingming Ha,Mingming Zhao
Publisher : Springer Nature
Page : 283 pages
File Size : 54,6 Mb
Release : 2023-01-21
Category : Technology & Engineering
ISBN : 9789811972911

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Advanced Optimal Control and Applications Involving Critic Intelligence by Ding Wang,Mingming Ha,Mingming Zhao Pdf

This book intends to report new optimal control results with critic intelligence for complex discrete-time systems, which covers the novel control theory, advanced control methods, and typical applications for wastewater treatment systems. Therein, combining with artificial intelligence techniques, such as neural networks and reinforcement learning, the novel intelligent critic control theory as well as a series of advanced optimal regulation and trajectory tracking strategies are established for discrete-time nonlinear systems, followed by application verifications to complex wastewater treatment processes. Consequently, developing such kind of critic intelligence approaches is of great significance for nonlinear optimization and wastewater recycling. The book is likely to be of interest to researchers and practitioners as well as graduate students in automation, computer science, and process industry who wish to learn core principles, methods, algorithms, and applications in the field of intelligent optimal control. It is beneficial to promote the development of intelligent optimal control approaches and the construction of high-level intelligent systems.

Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems

Ding Wang,Chaoxu Mu
Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems Book PDF

Author : Ding Wang,Chaoxu Mu
Publisher : Springer
Page : 307 pages
File Size : 48,8 Mb
Release : 2018-08-10
Category : Technology & Engineering
ISBN : 9789811312533

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Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems by Ding Wang,Chaoxu Mu Pdf

This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical engineering wishing to learn about the fundamental principles, methods, algorithms, and applications in the field of robust adaptive critic control. In addition, it promotes the development of robust adaptive critic control approaches, and the construction of higher-level intelligent systems.

Robust Adaptive Dynamic Programming

Yu Jiang,Zhong-Ping Jiang
Robust Adaptive Dynamic Programming Book PDF

Author : Yu Jiang,Zhong-Ping Jiang
Publisher : John Wiley & Sons
Page : 216 pages
File Size : 40,7 Mb
Release : 2017-05-08
Category : Science
ISBN : 9781119132646

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Robust Adaptive Dynamic Programming by Yu Jiang,Zhong-Ping Jiang Pdf

A comprehensive look at state-of-the-art ADP theory and real-world applications This book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven optimal control design challenges arising from both parametric and dynamic uncertainties. Traditional model-based approaches leave much to be desired when addressing the challenges posed by the ever-increasing complexity of real-world engineering systems. An alternative which has received much interest in recent years are biologically-inspired approaches, primarily RADP. Despite their growing popularity worldwide, until now books on ADP have focused nearly exclusively on analysis and design, with scant consideration given to how it can be applied to address robustness issues, a new challenge arising from dynamic uncertainties encountered in common engineering problems. Robust Adaptive Dynamic Programming zeros in on the practical concerns of engineers. The authors develop RADP theory from linear systems to partially-linear, large-scale, and completely nonlinear systems. They provide in-depth coverage of state-of-the-art applications in power systems, supplemented with numerous real-world examples implemented in MATLAB. They also explore fascinating reverse engineering topics, such how ADP theory can be applied to the study of the human brain and cognition. In addition, the book: Covers the latest developments in RADP theory and applications for solving a range of systems’ complexity problems Explores multiple real-world implementations in power systems with illustrative examples backed up by reusable MATLAB code and Simulink block sets Provides an overview of nonlinear control, machine learning, and dynamic control Features discussions of novel applications for RADP theory, including an entire chapter on how it can be used as a computational mechanism of human movement control Robust Adaptive Dynamic Programming is both a valuable working resource and an intriguing exploration of contemporary ADP theory and applications for practicing engineers and advanced students in systems theory, control engineering, computer science, and applied mathematics.

Control of Complex Systems

Kyriakos Vamvoudakis,Sarangapani Jagannathan
Control of Complex Systems Book PDF

Author : Kyriakos Vamvoudakis,Sarangapani Jagannathan
Publisher : Butterworth-Heinemann
Page : 762 pages
File Size : 42,9 Mb
Release : 2016-07-27
Category : Technology & Engineering
ISBN : 9780128054376

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Control of Complex Systems by Kyriakos Vamvoudakis,Sarangapani Jagannathan Pdf

In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: “Introduction and Background on Control Theory”, “Adaptive Control and Neuroscience”, “Adaptive Learning Algorithms”, “Cyber-Physical Systems and Cooperative Control”, “Applications”. The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists. This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems. • Collection of chapters from several well-known professors and researchers that will showcase their recent work • Presents different state-of-the-art control approaches and theory for complex systems • Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams • Real system examples and figures throughout, make ideas concrete Includes chapters from several well-known professors and researchers that showcases their recent work Presents different state-of-the-art control approaches and theory for complex systems Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems

Neural Information Processing

Akira Hirose,Seiichi Ozawa,Kenji Doya,Kazushi Ikeda,Minho Lee,Derong Liu
Neural Information Processing Book PDF

Author : Akira Hirose,Seiichi Ozawa,Kenji Doya,Kazushi Ikeda,Minho Lee,Derong Liu
Publisher : Springer
Page : 651 pages
File Size : 47,5 Mb
Release : 2016-09-30
Category : Computers
ISBN : 9783319466750

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Neural Information Processing by Akira Hirose,Seiichi Ozawa,Kenji Doya,Kazushi Ikeda,Minho Lee,Derong Liu Pdf

The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms.

Nonlinear Systems

Dongbin Lee,Christos Volos,Timothy Burg
Nonlinear Systems Book PDF

Author : Dongbin Lee,Christos Volos,Timothy Burg
Publisher : BoD – Books on Demand
Page : 366 pages
File Size : 43,5 Mb
Release : 2016-10-19
Category : Mathematics
ISBN : 9789535127147

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Nonlinear Systems by Dongbin Lee,Christos Volos,Timothy Burg Pdf

The book consists mainly of two parts: Chapter 1 - Chapter 7 and Chapter 8 - Chapter 14. Chapter 1 and Chapter 2 treat design techniques based on linearization of nonlinear systems. An analysis of nonlinear system over quantum mechanics is discussed in Chapter 3. Chapter 4 to Chapter 7 are estimation methods using Kalman filtering while solving nonlinear control systems using iterative approach. Optimal approaches are discussed in Chapter 8 with retarded control of nonlinear system in singular situation, and Chapter 9 extends optimal theory to H-infinity control for a nonlinear control system.Chapters 10 and 11 present the control of nonlinear dynamic systems, twin-rotor helicopter and 3D crane system, which are both underactuated, cascaded dynamic systems. Chapter 12 applies controls to antisynchronization/synchronization in the chaotic models based on Lyapunov exponent theorem, and Chapter 13 discusses developed stability analytic approaches in terms of Lyapunov stability. The analysis of economic activities, especially the relationship between stock return and economic growth, is presented in Chapter 14.

Reinforcement Learning

Jinna Li,Frank L. Lewis,Jialu Fan
Reinforcement Learning Book PDF

Author : Jinna Li,Frank L. Lewis,Jialu Fan
Publisher : Springer Nature
Page : 318 pages
File Size : 44,5 Mb
Release : 2023-07-24
Category : Technology & Engineering
ISBN : 9783031283949

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Reinforcement Learning by Jinna Li,Frank L. Lewis,Jialu Fan Pdf

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.

Discrete-Time Inverse Optimal Control for Nonlinear Systems

Edgar Sanchez,Fernando Ornelas-Tellez
Discrete Time Inverse Optimal Control for Nonlinear Systems Book PDF

Author : Edgar Sanchez,Fernando Ornelas-Tellez
Publisher : Unknown
Page : 268 pages
File Size : 43,5 Mb
Release : 2016
Category : Mathematics
ISBN : OCLC:1105790391

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Discrete-Time Inverse Optimal Control for Nonlinear Systems by Edgar Sanchez,Fernando Ornelas-Tellez Pdf

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.

Neural Network Control of Nonlinear Discrete-Time Systems

Jagannathan Sarangapani
Neural Network Control of Nonlinear Discrete Time Systems Book PDF

Author : Jagannathan Sarangapani
Publisher : CRC Press
Page : 624 pages
File Size : 55,9 Mb
Release : 2018-10-03
Category : Technology & Engineering
ISBN : 9781420015454

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Neural Network Control of Nonlinear Discrete-Time Systems by Jagannathan Sarangapani Pdf

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

Adaptive Dynamic Programming with Applications in Optimal Control

Derong Liu,Qinglai Wei,Ding Wang,Xiong Yang,Hongliang Li
Adaptive Dynamic Programming with Applications in Optimal Control Book PDF

Author : Derong Liu,Qinglai Wei,Ding Wang,Xiong Yang,Hongliang Li
Publisher : Springer
Page : 594 pages
File Size : 52,9 Mb
Release : 2017-01-04
Category : Technology & Engineering
ISBN : 9783319508153

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Adaptive Dynamic Programming with Applications in Optimal Control by Derong Liu,Qinglai Wei,Ding Wang,Xiong Yang,Hongliang Li Pdf

This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors’ work: • renewable energy scheduling for smart power grids;• coal gasification processes; and• water–gas shift reactions. Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.

Neural Systems for Control

Omid Omidvar,David L. Elliott
Neural Systems for Control Book PDF

Author : Omid Omidvar,David L. Elliott
Publisher : Elsevier
Page : 375 pages
File Size : 40,9 Mb
Release : 1997-02-24
Category : Computers
ISBN : 9780080537399

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Neural Systems for Control by Omid Omidvar,David L. Elliott Pdf

Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis