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This book intends to report new optimal control results with critic intelligence for complex discrete-time systems, which covers the novel control theory, advanced control methods, and typical applications for wastewater treatment systems. Therein, combining with artificial intelligence techniques, such as neural networks and reinforcement learning, the novel intelligent critic control theory as well as a series of advanced optimal regulation and trajectory tracking strategies are established for discrete-time nonlinear systems, followed by application verifications to complex wastewater treatment processes. Consequently, developing such kind of critic intelligence approaches is of great significance for nonlinear optimization and wastewater recycling. The book is likely to be of interest to researchers and practitioners as well as graduate students in automation, computer science, and process industry who wish to learn core principles, methods, algorithms, and applications in the field of intelligent optimal control. It is beneficial to promote the development of intelligent optimal control approaches and the construction of high-level intelligent systems.
List of contents
A Survey of Robust Adaptive Critic Control Design.- Robust Optimal Control of Nonlinear Systems with Matched Uncertainties.- Observer-Based Online Adaptive Regulation for a Class of Uncertain Nonlinear Systems.- Adaptive Tracking Control of Nonlinear Systems Subject to Matched Uncertainties.- Event-Triggered Robust Stabilization Incorporating an Adaptive Critic Mechanism.- An Improved Adaptive Optimal Regulation Framework with Robust Control Synthesis.- Robust Stabilization and Trajectory Tracking of General Uncertain Nonlinear Systems.- Event-Triggered Nonlinear H Control Design via an Improved Critic Learning Strategy.- Intelligent Critic Control with Disturbance Attenuation for a Micro-Grid System.- Sliding Mode Design for Load Frequency Control with Power System Applications.
About the author
Ding Wang (Senior Member, IEEE) received the Ph.D. degree in control theory and control engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2012. From 2015 to 2017, he was Visiting Scholar at the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA. He was Associate Professor at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He is currently Professor at the Faculty of Information Technology, Beijing University of Technology. He has authored or co-authored over 120 journal and conference papers and four monographs. His current research interests include adaptive critic control with industrial applications, reinforcement learning, and intelligent systems. Dr. Wang was a recipient of the Excellent Doctoral Dissertation Award of Chinese Academy of Sciences in 2013 and a nomination of the Excellent Doctoral Dissertation Award of Chinese Association of Automation in 2014. He was selected for the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology in 2017 and also selected for the Youth Innovation Promotion Association of the Chinese Academy of Sciences in 2018. He was Finance Chair of the 12th World Congress on Intelligent Control and Automation in 2016 and Publications Chair of the 24th International Conference on Neural Information Processing in 2017. He currently or formerly serves as Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Neural Networks, International Journal of Robust and Nonlinear Control, Neurocomputing, and Acta Automatica Sinica.
Mingming Ha received the B.E. and M.E. degrees from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2016 and 2019, respectively, where he is currently pursuing the Ph.D. degree in control science and engineering. His research interests include optimal control, adaptive dynamic programming, and reinforcement learning.
Mingming Zhao received the B.E. degree in automation from Henan Polytechnic University, Jiaozuo, China, in 2019, and the M.E. degree in control engineering from the Beijing University of Technology, Beijing, China, in 2022, where he is currently pursuing the Ph.D. degree in control science and engineering. His research interest covers reinforcement learning and intelligent control.
