Neural Network Based Adaptive Control Of Uncertain Nonlinear Systems
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Neural Network Based Adaptive Control Of Uncertain Nonlinear Systems
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Author : Kasra Esfandiari
language : en
Publisher: Springer Nature
Release Date : 2021-06-18
Neural Network Based Adaptive Control Of Uncertain Nonlinear Systems written by Kasra Esfandiari and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-18 with Technology & Engineering categories.
The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Nonlinear And Adaptive Control With Applications
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Author : Alessandro Astolfi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-06
Nonlinear And Adaptive Control With Applications written by Alessandro Astolfi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-06 with Technology & Engineering categories.
The authors here provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. They employ a new tool based on the ideas of system immersion and manifold invariance. New algorithms are delivered for the construction of robust asymptotically-stabilizing and adaptive control laws for nonlinear systems. The methods proposed lead to modular schemes that are easier to tune than their counterparts obtained from Lyapunov redesign.
Nonlinear Control Of Engineering Systems
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Author : Warren E. Dixon
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Nonlinear Control Of Engineering Systems written by Warren E. Dixon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Technology & Engineering categories.
Recent advancements in Lyapunov-based design and analysis techniques have applications to a broad class of engineering systems, including mechanical, electrical, robotic, aerospace, and underactuated systems. This book provides a practical yet rigorous development of nonlinear, Lyapunov-based tools and their use in the solution of control-theoretic problems. Rich in motivating examples and new design techniques, the text balances theoretical foundations and real-world implementation. Features include: * Control designs for a broad class of engineering systems * Presentation of adaptive and learning control methods for uncertain nonlinear systems * Experimental testbed descriptions and results that guide the reader toward techniques for further research * Development of necessary mathematical background in each chapter; additional mathematical prerequisites contained in two appendices Intended for readers who have some knowledge of undergraduate systems theory, the book includes a wide range of applications making it suitable for an extensive audience. Graduate students and researchers in control systems, robotics, and applied mathematics, as well as professional engineers will appreciate the work's combination of theoretical underpinnings and current and emerging engineering applications.
Neural Network Control Of Nonlinear Discrete Time Systems
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Author : Jagannathan Sarangapani
language : en
Publisher: CRC Press
Release Date : 2018-10-03
Neural Network Control Of Nonlinear Discrete Time Systems written by Jagannathan Sarangapani and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-03 with Technology & Engineering categories.
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 Neural Network Control Of Robotic Manipulators
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Author : Tong Heng Lee
language : en
Publisher: World Scientific
Release Date : 1998
Adaptive Neural Network Control Of Robotic Manipulators written by Tong Heng Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Introduction; Mathematical background; Dynamic modelling of robots; Structured network modelling of robots; Adaptive neural network control of robots; Neural network model reference adaptive control; Flexible joint robots; task space and force control; Bibliography; Computer simulation; Simulation software in C.
Neural Network Based Adaptive Control Of Uncertain And Unknown Nonlinear Systems
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Author : Anthony Calise
language : en
Publisher:
Release Date : 2001
Neural Network Based Adaptive Control Of Uncertain And Unknown Nonlinear Systems written by Anthony Calise and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Adaptive control systems categories.
Neural Network Based Adaptive Control Of Uncertain And Unknown Nonlinear Systems
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Author :
language : en
Publisher:
Release Date : 2001
Neural Network Based Adaptive Control Of Uncertain And Unknown Nonlinear Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Our main accomplishment this past year has been to finalize and apply two approaches to output feedback adaptive control. The first is a direct adaptive approach, while the second uses a new error state observe. Both approaches overcome the limitation of earlier adaptive state observer based methods, which require that the order of the plant be known, and impose severe restrictions on the relative degree of regulated output variables. Within this context, we also have continued to exploit our approach for adaptive.
Adaptive Dynamic Programming Single And Multiple Controllers
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Author : Ruizhuo Song
language : en
Publisher: Springer
Release Date : 2018-12-28
Adaptive Dynamic Programming Single And Multiple Controllers written by Ruizhuo Song and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-28 with Technology & Engineering categories.
This book presents a class of novel optimal control methods and games schemes based on adaptive dynamic programming techniques. For systems with one control input, the ADP-based optimal control is designed for different objectives, while for systems with multi-players, the optimal control inputs are proposed based on games. In order to verify the effectiveness of the proposed methods, the book analyzes the properties of the adaptive dynamic programming methods, including convergence of the iterative value functions and the stability of the system under the iterative control laws. Further, to substantiate the mathematical analysis, it presents various application examples, which provide reference to real-world practices.
Systems With Hysteresis
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Author : Mark A. Krasnosel'skii
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Systems With Hysteresis written by Mark A. Krasnosel'skii and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Hysteresis phenomena are common in numerous physical, mechanical, ecological and biological systems. They reflect memory effects and process irreversibility. The use of hysteresis operators (hysterons) offers an approach to macroscopic modelling of the dynamics of phase transitions and rheological systems. The applications cover processes in electromagnetism, elastoplasticity and population dynamics in particular. Hysterons are also typical elements of control systems where they represent thermostats and other discontinuous controllers with memory. The book offers the first systematic mathematical treatment of hysteresis nonlinearities. Construction procedures are set up for hysterons in various function spaces, in continuous and discontinuous cases. A general theory of variable hysterons is developed, including identification and stability questions. Both deterministic and non-deterministic hysterons are considered, with applications to the study of feedback systems. Many of the results presented - mostly obtained by the authors and their scientific group - have not been published before. The book is essentially self contained and is addressed both to researchers and advanced students.
Zhang Time Discretization Ztd Formulas And Applications
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Author : Yunong Zhang
language : en
Publisher: CRC Press
Release Date : 2024-08-07
Zhang Time Discretization Ztd Formulas And Applications written by Yunong Zhang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-07 with Computers categories.
This book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas. The authors summarize and present the systematic derivations and complete research of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. This book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control. The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.