Model 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 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.

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.

Introduction. Recursive Parameter Estimation for Discrete-Time Systems. Dynamic Linearization Approach of Discrete-Time Nonlinear Systems. Model-Free Adaptive Control of SISO Discrete-Time Nonlinear Systems. Model-Free Adaptive Control of MIMO Discrete-Time Nonlinear Systems. Model-Free Adaptive Predictive Control. Model-Free Adaptive Iterative Learning Control. Model-Free Adaptive Control for Complex Connected Systems and Modularized Controller Design. Robustness of Model-Free Adaptive Control. Symmetric Similarity for Control System Design. Applications. Conclusions and Perspectives. References. Index.