Data-Driven Spatiotemporal Modeling and Control of Nonlinear Distributed Parameter Systems and Their Applications: (24 Intelligent Control and Learning Systems)

This book provides a systematic overview and classification on modeling and control of different types of Distributed Parameter Systems (DPSs), and develops new methods to tackle some of these unsolved problems, facilitating a better understanding of DPSs and providing references for practical problem-solving in the relevant fields. All these methods presented in this book are verified by specific scenarios. It is worth mentioning that in the context of disciplinary integration and artificial intelligence, the research ideas, methods and models in this book can be further integrated and developed. From application perspectives, they can also extend from traditional mechanical, electrical, and chemical fields to emerging fields of new energy, new materials, and multimodal information. Under the background of disciplinary integration and the development of artificial intelligence, the book will be beneficial to undergraduate and postgraduate students in interdisciplinary disciplines including manufacturing engineering, mechanical engineering, electrical engineering, computer engineering, and control engineering, etc. It is also intended for researchers and practical users in the fields of nonlinear dynamics, spatiotemporal modeling and intelligent control.

Table of Contents:
Introduction.- Spatiotemporal LS-SVM Modeling Approach for Nonlinear DPSs.- Multi-kernel Spatiotemporal Model for Large Operation Range.- Graphic Relation-based Spatiotemporal Model for Large Spatial Region.- Online Low-order Spatiotemporal LS-SVM Modeling Approach.- Online Spatiotemporal ELM Modeling Method.- Knowledge-based Spatiotemporal Fuzzy Modeling Approach.- Spatiotemporal Recurrent Neural Network Modeling Approach.- Convolution and Memory Network-based Spatiotemporal Model.- Data-driven Spatiotemporal Inverse Control Approach.- KPCA-based Spatiotemporal Model Predictive Control Approach.- Two-Layer Rules-based Spatiotemporal Fuzzy Control Strategy.- Conclusion and Prospects.

About the Author :
Dr. Bowen Xu received the B.E. and M.E. degrees in industrial engineering and mechanical engineering from the College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China, in 2013 and 2016, respectively. He obtained the doctoral degree from School of Mechanical and Electrical Engineering, Central South University, Changsha, China in 2023. He is an assistant researcher at the National University of Defense Technology, and his research interests include machine learning, parameter identification, distributed parameter processes, process model and control. Prof. Xinjiang Lu received the B.E. and M.E. degrees in mechanical engineering from the School of Mechanical and Electrical Engineering, Central South University, Changsha, China, in 2002 and 2005, respectively, and the Ph.D. degree in mechanical engineering from the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, in 2010, all in mechanical engineering. He is currently a Professor with the School of Mechanical and Electrical Engineering, Central South University. His research interests include machine learning, image processing, process modeling and control, and robot. Dr. Lu was a recipient of the Excellent Thesis Award for Master's Degree of Hunan Province in 2007, the Hiwin Doctoral Dissertation Award in 2011, the New Century Excellent Talents Award by the Chinese Ministry of Education in 2013, and the Hunan Provincial Science Fund for Distinguished Young Scholars in 2019. He served for editorial board membership of three international Journals.