Learning Based Adaptive Control

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  • Publisher : Butterworth-Heinemann
  • Release : 02 August 2016
  • ISBN : 9780128031513
  • Page : 282 pages
  • Rating : 4.5/5 from 103 voters

Learning Based Adaptive Control Book PDF summary

Adaptive 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.

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Learning-Based Adaptive Control

Learning-Based Adaptive Control
  • Author : Mouhacine Benosman
  • Publisher : Butterworth-Heinemann
  • Release Date : 2016-08-02
  • ISBN : 9780128031513
DOWNLOAD BOOKLearning-Based Adaptive Control

Adaptive 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

Evolutionary Learning Algorithms for Neural Adaptive Control

Evolutionary Learning Algorithms for Neural Adaptive Control
  • Author : Dimitris C. Dracopoulos
  • Publisher : Springer
  • Release Date : 2013-12-21
  • ISBN : 9781447109037
DOWNLOAD BOOKEvolutionary Learning Algorithms for Neural Adaptive Control

Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.

Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles
  • Author : Draguna Vrabie,Kyriakos G. Vamvoudakis,Frank L. Lewis
  • Publisher : IET
  • Release Date : 2013
  • ISBN : 9781849194891
DOWNLOAD BOOKOptimal Adaptive Control and Differential Games by Reinforcement Learning Principles

This book gives an exposition of recently developed approximate dynamic programming (ADP) techniques for decision and control in human engineered systems.

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems
  • Author : Kasra Esfandiari,Farzaneh Abdollahi,Heidar A. Talebi
  • Publisher : Springer Nature
  • Release Date : 2021-06-18
  • ISBN : 9783030731366
DOWNLOAD BOOKNeural Network-Based Adaptive Control of Uncertain Nonlinear Systems

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

Learning-based Adaptive Control

Learning-based Adaptive Control
  • Author : Mouhacine Benosman
  • Publisher : Butterworth-Heinemann
  • Release Date : 2016-07-11
  • ISBN : 0128031360
DOWNLOAD BOOKLearning-based Adaptive Control

Adaptive 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 for Adaptive and Reactive Robot Control

Learning for Adaptive and Reactive Robot Control
  • Author : Aude Billard,Sina Mirrazavi,Nadia Figueroa
  • Publisher : MIT Press
  • Release Date : 2022-02-08
  • ISBN : 9780262367011
DOWNLOAD BOOKLearning for Adaptive and Reactive Robot Control

Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer

Adaptive Control of Nonsmooth Dynamic Systems

Adaptive Control of Nonsmooth Dynamic Systems
  • Author : Gang Tao,Frank L. Lewis
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-04-17
  • ISBN : 9781447136873
DOWNLOAD BOOKAdaptive Control of Nonsmooth Dynamic Systems

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.

Control Systems

Control Systems
  • Author : Jitendra R. Raol,Ramakalyan Ayyagari
  • Publisher : CRC Press
  • Release Date : 2019-07-12
  • ISBN : 9781351170789
DOWNLOAD BOOKControl Systems

Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional

Applications of Neural Adaptive Control Technology

Applications of Neural Adaptive Control Technology
  • Author : Jens Kalkkuhl,Rafal Zbikowski
  • Publisher : World Scientific
  • Release Date : 1997
  • ISBN : 9810231512
DOWNLOAD BOOKApplications of Neural Adaptive Control Technology

This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9-10, 1996, in Berlin. The workshop was organised in connection with a three-year European-Union-funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland).The NACT project, which began on 1 April 1994, is a study of the fundamental properties of neural-network-based adaptive control systems. Where possible, links with traditional adaptive control systems are

Model Free Adaptive Control

Model Free Adaptive Control
  • Author : Zhongsheng Hou,Shangtai Jin
  • Publisher : CRC Press
  • Release Date : 2013-09-24
  • ISBN : 9781466594180
DOWNLOAD BOOKModel Free Adaptive Control

Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it more applicable for many practical plants. This book covers new concepts, including pseudo partial derivative, pseudo gradient, pseudo Jacobian matrix, and generalized

L1 Adaptive Control Theory

L1 Adaptive Control Theory
  • Author : Naira Hovakimyan,Chengyu Cao
  • Publisher : SIAM
  • Release Date : 2010-09-30
  • ISBN : 9780898717044
DOWNLOAD BOOKL1 Adaptive Control Theory

Contains results not yet published in technical journals and conference proceedings.

Machine Vision Inspection Systems, Machine Learning-Based Approaches

Machine Vision Inspection Systems, Machine Learning-Based Approaches
  • Author : Muthukumaran Malarvel,Soumya Ranjan Nayak,Prasant Kumar Pattnaik,Surya Narayan Panda
  • Publisher : John Wiley & Sons
  • Release Date : 2021-01-14
  • ISBN : 9781119786108
DOWNLOAD BOOKMachine Vision Inspection Systems, Machine Learning-Based Approaches

Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day's the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process. This volume 2 covers machine learning-based approaches in MVIS

Learning-Based Control

Learning-Based Control
  • Author : Zhong-Ping Jiang,Tao Bian,Weinan Gao
  • Publisher : Now Publishers
  • Release Date : 2020-12-07
  • ISBN : 1680837524
DOWNLOAD BOOKLearning-Based Control

The recent success of Reinforcement Learning and related methods can be attributed to several key factors. First, it is driven by reward signals obtained through the interaction with the environment. Second, it is closely related to the human learning behavior. Third, it has a solid mathematical foundation. Nonetheless, conventional Reinforcement Learning theory exhibits some shortcomings particularly in a continuous environment or in considering the stability and robustness of the controlled process. In this monograph, the authors build on Reinforcement Learning

Advances in Aerospace Guidance, Navigation and Control

Advances in Aerospace Guidance, Navigation and Control
  • Author : Qiping Chu,Bob Mulder,Daniel Choukroun,Erik-Jan van Kampen,Coen de Visser,Gertjan Looye
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-11-18
  • ISBN : 9783642382536
DOWNLOAD BOOKAdvances in Aerospace Guidance, Navigation and Control

Following the successful 1st CEAS (Council of European Aerospace Societies) Specialist Conference on Guidance, Navigation and Control (CEAS EuroGNC) held in Munich, Germany in 2011, Delft University of Technology happily accepted the invitation of organizing the 2nd CEAS EuroGNC in Delft, The Netherlands in 2013. The goal of the conference is to promote new advances in aerospace GNC theory and technologies for enhancing safety, survivability, efficiency, performance, autonomy and intelligence of aerospace systems using on-board sensing, computing and systems. A great push

Neural Networks for Control

Neural Networks for Control
  • Author : W. Thomas Miller,Richard S. Sutton,Paul J. Werbos
  • Publisher : MIT Press
  • Release Date : 1995
  • ISBN : 026263161X
DOWNLOAD BOOKNeural Networks for Control

Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers