1st Edition

Model Free Adaptive Control Theory and Applications

By Zhongsheng Hou, Shangtai Jin Copyright 2014
    398 Pages 145 B/W Illustrations
    by CRC Press

    398 Pages 145 B/W Illustrations
    by CRC Press

    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.

    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

    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.