The book summarizes theory and applications of data-driven model-free adaptive control (MFAC) which is different from the traditional adaptive control. The traditional unmodeled dynamics do not exist in MFAC framework. In addition, MFAC is suitable for many practical applications since it is easily implemented and has strong robustness. By reading this book, readers become familiar with MFAC in a short time, and can quickly carry out their independent research and applications.
The book summarizes theory and applications of data-driven model-free adaptive control (MFAC) which is different from the traditional adaptive control. The traditional unmodeled dynamics do not exist in MFAC framework. In addition, MFAC is suitable for many practical applications since it is easily implemented and has strong robustness. By reading this book, readers become familiar with MFAC in a short time, and can quickly carry out their independent research and applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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.
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.
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