Adaptive Robust Control with Statistical Learning
Author: Theerawat Bhudisaksang
Publisher:
Published: 2022
Total Pages: 0
ISBN-13:
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Adaptive Robust Control Systems
Author: Anh Tuan Le
Publisher: BoD – Books on Demand
Published: 2018-03-07
Total Pages: 364
ISBN-13: 9535137964
DOWNLOAD EBOOKThis book focuses on the applications of robust and adaptive control approaches to practical systems. The proposed control systems hold two important features: (1) The system is robust with the variation in plant parameters and disturbances (2) The system adapts to parametric uncertainties even in the unknown plant structure by self-training and self-estimating the unknown factors. The various kinds of robust adaptive controls represented in this book are composed of sliding mode control, model-reference adaptive control, gain-scheduling, H-infinity, model-predictive control, fuzzy logic, neural networks, machine learning, and so on. The control objects are very abundant, from cranes, aircrafts, and wind turbines to automobile, medical and sport machines, combustion engines, and electrical machines.
Robust Adaptive Dynamic Programming
Author: Yu Jiang
Publisher: John Wiley & Sons
Published: 2017-04-25
Total Pages: 237
ISBN-13: 1119132665
DOWNLOAD EBOOKA 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.
Adaptive Robust Control for Planar Snake Robots
Author: Joyjit Mukherjee
Publisher: Springer Nature
Published: 2021-05-11
Total Pages: 179
ISBN-13: 3030714608
DOWNLOAD EBOOKThis book shows how a conventional multi-layered approach can be used to control a snake robot on a desired path while moving on a flat surface. To achieve robustness to unknown variations in surface conditions, it explores various adaptive robust control methods. The authors propose a sliding-mode control approach designed to achieve robust maneuvering for bounded uncertainty with a known upper bound. The control is modified by addition of an adaptation law to alleviate the overestimation problem of the switching gain as well as to circumvent the requirement for knowledge regarding the bounds of uncertainty. The book works toward non-conservativeness, achieving efficient tracking in the presence of slowly varying uncertainties with a specially designed framework for time-delayed control. It shows readers how to extract superior performance from their snake robots with an approach that allows robustness toward bounded time-delayed estimation errors. The book also demonstrates how the multi-layered control framework can be simplified by employing differential flatness for such a system. Finally, the mathematical model of a snake robot moving inside a uniform channel using only side-wall contact is discussed. The model has further been employed to demonstrate adaptive robust control design for such a motion. Using numerous illustrations and tables, Adaptive Robust Control for Planar Snake Robots will interest researchers, practicing engineers and postgraduate students working in the field of robotics and control systems.
Adaptive Control for Robotic Manipulators
Author: Dan Zhang
Publisher: CRC Press
Published: 2017-02-03
Total Pages: 407
ISBN-13: 1351678922
DOWNLOAD EBOOKThe robotic mechanism and its controller make a complete system. As the robotic mechanism is reconfigured, the control system has to be adapted accordingly. The need for the reconfiguration usually arises from the changing functional requirements. This book will focus on the adaptive control of robotic manipulators to address the changed conditions. The aim of the book is to summarise and introduce the state-of-the-art technologies in the field of adaptive control of robotic manipulators in order to improve the methodologies on the adaptive control of robotic manipulators. Advances made in the past decades are described in the book, including adaptive control theories and design, and application of adaptive control to robotic manipulators.
Adaptive Robust Control Systems
Author: Le Anh Tuan
Publisher:
Published: 2018
Total Pages: 362
ISBN-13: 9789535140702
DOWNLOAD EBOOKThis book focuses on the applications of robust and adaptive control approaches to practical systems. The proposed control systems hold two important features: (1) The system is robust with the variation in plant parameters and disturbances (2) The system adapts to parametric uncertainties even in the unknown plant structure by self-training and self-estimating the unknown factors. The various kinds of robust adaptive controls represented in this book are composed of sliding mode control, model-reference adaptive control, gain-scheduling, H-infinity, model-predictive control, fuzzy logic, neural networks, machine learning, and so on. The control objects are very abundant, from cranes, aircrafts, and wind turbines to automobile, medical and sport machines, combustion engines, and electrical machines.
Safe Adaptive Control
Author: Margareta Stefanovic
Publisher: Springer
Published: 2011-02-04
Total Pages: 153
ISBN-13: 184996453X
DOWNLOAD EBOOKSafe Adaptive Control gives a formal and complete algorithm for assuring the stability of a switched control system when at least one of the available candidate controllers is stabilizing. The possibility of having an unstable switched system even in the presence of a stabilizing candidate controller is demonstrated by referring to several well-known adaptive control approaches, where the system goes unstable when a large mismatch between the unknown plant and the available models exists ("plant-model mismatch instability"). Sufficient conditions for this possibility to be avoided are formulated, and a "recipe" to be followed by the control system designer to guarantee stability and desired performance is provided. The problem is placed in a standard optimization setting. Unlike the finite controller sets considered elsewhere, the candidate controller set is allowed to be continuously parametrized so that it can deal with plants with a very large range of uncertainties.
Learning-Based Adaptive Control
Author: Mouhacine Benosman
Publisher: Butterworth-Heinemann
Published: 2016-08-02
Total Pages: 284
ISBN-13: 0128031514
DOWNLOAD EBOOKAdaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques. Compares and blends Model-free and Model-based learning algorithms. Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.
Reinforcement Learning
Author: Jinna Li
Publisher: Springer Nature
Published: 2023-07-24
Total Pages: 318
ISBN-13: 3031283945
DOWNLOAD EBOOKThis 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.
L1 Adaptive Control Theory
Author: Naira Hovakimyan
Publisher: SIAM
Published: 2010-09-30
Total Pages: 333
ISBN-13: 0898717043
DOWNLOAD EBOOKContains results not yet published in technical journals and conference proceedings.
