1st Edition

Model Free Adaptive Control
Theory and Applications





ISBN 9781138033962
Published November 16, 2016 by CRC Press
375 Pages 145 B/W Illustrations

USD $67.95

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Book Description

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 Lipschitz conditions, etc.; dynamic linearization approaches for nonlinear systems, such as compact-form dynamic linearization, partial-form dynamic linearization, and full-form dynamic linearization; a series of control system design methods, including MFAC prototype, model-free adaptive predictive control, model-free adaptive iterative learning control, and the corresponding stability analysis and typical applications in practice. In addition, some other important issues related to MFAC are also discussed. They are the MFAC for complex connected systems, the modularized controller designs between MFAC and other control methods, the robustness of MFAC, and the symmetric similarity for adaptive control system design.

The book is written for researchers who are interested in control theory and control engineering, senior undergraduates and graduated students in engineering and applied sciences, as well as professional engineers in process control.

Table of Contents

Introduction
Model-Based Control
Data-Driven Control
Preview of the Book
Recursive Parameter Estimation for Discrete-Time Systems
Introduction
Parameter Estimation Algorithm for Linearly Parameterized Systems
Parameter Estimation Algorithm for Nonlinearly Parameterized Systems
Conclusions
Dynamic Linearization Approach of Discrete-Time Nonlinear Systems
Introduction
SISO Discrete-Time Nonlinear Systems
MIMO Discrete-Time Nonlinear Systems
Conclusions
Model-Free Adaptive Control of SISO Discrete-Time Nonlinear Systems
Introduction
CFDL Data Model Based MFAC
PFDL Data Model Based MFAC
FFDL Data Model Based MFAC
Conclusions
Model-Free Adaptive Control of MIMO Discrete-Time Nonlinear Systems
Introduction
CFDL Data Model Based MFAC
PFDL Data Model Based MFAC
FFDL Data Model Based MFAC
Conclusions
Model-Free Adaptive Predictive Control
Introduction
CFDL Data Model Based MFAPC
PFDL Data Model Based MFAPC
FFDL Data Model Based MFAPC
Conclusions
Model-Free Adaptive Iterative Learning Control
Introduction
CFDL Data Model Based MFAILC
Conclusions
Model-Free Adaptive Control for Complex Connected Systems and Modularized Controller Design
Introduction
MFAC for Complex Connected Systems
Modularized Controller Design
Conclusions
Robustness of Model-Free Adaptive Control
Introduction
MFAC in the Presence of Output Measurement Noise
MFAC in the Presence of Data Dropouts
Conclusions
Symmetric Similarity for Control System Design
Introduction
Symmetric Similarity for Adaptive Control Design
Similarity between MFAC and MFAILC
Similarity between Adaptive Control and Iterative Learning Control
Conclusions
Applications
Introduction
Three-Tank Water System
Permanent Magnet Linear Motor
Freeway Traffic System
Welding Process
MW Grade Wind Turbine
Conclusions
Conclusions and Perspectives
Conclusions
Perspectives
References

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Author(s)

Biography

Zhongsheng Hou received his bachelor’s and master’s degrees from Jilin University of Technology, Changchun, China, in 1983 and 1988, and his PhD from Northeastern University, Shenyang, China, in 1994. In 1997, he joined Beijing Jiaotong University, Beijing, China, and is currently a full professor and the founding director of the Advanced Control Systems Lab, and the dean of the Department of Automatic Control. His research interests are in the fields of data-driven control, model-free adaptive control, iterative learning control, and intelligent transportation systems. He has over 110 peer-reviewed journal papers published and over 120 papers in prestigious conference proceedings. His personal website is available at acsl.bjtu.edu.cn.

Shangtai Jin received his BS, MS, and PhD degrees from Beijing Jiaotong University, Beijing, China, in 1999, 2004, and 2009, respectively. He is currently a lecturer with Beijing Jiaotong University. His research interests include model-free adaptive control, iterative learning control, and intelligent transportation systems.