Advances in Discrete-Time Sliding Mode Control Theory and Applications

The focus of this book is on the design of a specific control strategy using digital computers. This control strategy referred to as Sliding Mode Control (SMC), has its roots in (continuous-time) relay control. This book aims to explain recent investigations' output in the field of discrete-time sliding mode control (DSMC). The book starts by explaining a new robust LMI-based (state-feedback and observer-based output-feedback) DSMC including a new scheme for sparsely distributed control. It includes a novel event-driven control mechanism, called actuator-based event-driven scheme, using a synchronized-rate biofeedback system for heart rate regulation during cycle-ergometer. Key Features: Focuses on LMI-based SMC (sliding mode control) for uncertain discrete-time system using novel nonlinear components in the control law Makes reader understand the techniques of designing a discrete controller based on the flexible sliding functions Proposes new algorithms for sparsifying control and observer network through multi-objective optimization frameworks Discusses a framework for the design of SMC for two-dimensional systems along with analyzing the controllability of two-dimensional systems Discusses novel schemes for sparsifying the control network

1 Introduction1.1 Continuous-time SMC 1.2 Regular form-based DSMC 1.3 Summary I LMI-Based Discrete-Time Sliding Mode Control 2 LMI-based SF DSMC 2.1 Introduction2.2 Problem Formulation2.3 Design of Discrete-time SMC 2.4 Disturbance Estimate in Control Law 2.5 Simulation Results 2.6 Conclusions 3 LMI-based output feedback DSMC3.1 Introduction 3.2 Problem Formulation 3.3 Observer-Based output feedback DSMC 3.4 Simulation Results 3.5 Conclusions II DSMC for NCSs involving packet losses 4 NCSs with measurement packet losses4.1 Introduction4.2 Problem Formulation and Preliminaries4.3 Stochastic Sliding Mode Control 4.4 Variable Structure Controller Considerations 4.5 Simulation Results 4.6 Conclusions 5 NCSs with actuation and measurement packet losses 5.1 Introduction 5.2 Problem Formulation and Preliminaries5.3 Stochastic sliding mode control 5.4 Numerical examples 5.5 Conclusions III Sparse Sliding Mode Control for Large Scale NCSs 6 Sparse DSMC for NCSs6.1 Introduction 6.2 Problem Formulation and Preliminaries 6.3 Spatially Decentralized Sliding Mode Control 6.4 Stability Analysis 6.5 Sparsifying the Control Network Structure 6.6 Numerical examples 6.7 Conclusions 7 Optimal sparse SMC for NCSs 7.1 Introduction 7.2 Problem Formulation and Preliminaries 7.3 Optimal Structured SMC Design Problem 7.4 Sparsification of Control Network7.5 Numerical Examples 7.6 Solving LQ SOF problem 7.7 Reweighted `1 minimization algorithm 7.8 Conclusions IV DSMC for Two-Dimensional Systems8 DSMC for 2D Systems 8.1 Introduction8.2 Problem Formulation8.2.1 New 1D form of 2D first FM model8.3 DSMC for 1D Discrete Vector Form8.4 Simulation Results8.5 Conclusions 9 Controllability Analysis of 2D systems9.1 Introduction9.2 WAM model of First FM Model9.3 Controllability analysis of the New Model in 8.2.19.4 Numerical Example9.5 Conclusions V Integral DSMC for Heart Rate Regulation 10 HR Regulation During Cycle-Ergometer Exercise10.1 Introduction10.2 Methods10.3 Results10.4 Discussion10.5 ConclusionsBibliographyIndex

Ahmadreza Argha received B.S. and M.S. degrees in Electrical Engineering from Shiraz University, Iran, in 2003 and 2006, respectively. He is currently a Ph.D. student at UTS in Sydney, Australia (will be graduated in June 2016). His research interests include control and observer synthesis using convex methods for large-scale systems and networked control systems involving random time delay and packet losses, control applications to large-scale networks including power systems, Sliding Mode Control, Robust control, Optimal control, Sparsely distributed control, and further, biomedical system modelling and control. Steven W. Su received the B.S. and M.S. degrees from Harbin Institute of Technology (HIT), Harbin, China, in 1990 and 1993, respectively, and the Ph.D. degree from the Research School of Information Sciences and Engineering (RSISE), The Australian National University (ANU), Canberra, A.C.T., Australia, in 2002. From 2002 to 2004, he was a Postdoctoral Research Fellow in the School of Chemical Engineering and Industrial Chemistry, The University of New South Wales (UNSW), Sydney, NSW, Australia, where he was a Visiting Lecturer at the Biomedical System Laboratory, School of Electrical Engineering, and from 2004 to 2006, a Research Fellow in the School of Electrical Engineering and Telecommunications. He is currently an Associate Professor in the School of EMMS, and a core member of the Center for Health Technologies at UTS, Sydney. His current research interests include biomedical system modeling and control, robust and adaptive control, fault tolerant control, nonlinear process control, and navigation system design. is currently an Associate Professor in the School of EMMS, and a core member of the Center for Health Technologies at UTS. Li Li received his B.S. degree from Huazhong University of Science and Technology in 1996, M.S. degree from Tsinghua University in 1999, and Ph.D. degree from University of California, Los Angeles in 2005. From 2005 to 2007 he was a research associate at the University of New South Wales at the Australian Defence Force Academy (UNSW@ADFA). From 2007 to 2011, he was a researcher at the National ICT Australia, Victoria Research Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne. He joined UTS in 2011 and currently he is a Senior Lecturer. Dr Li held several visiting positions at Beijing Institute of Technology, Tsinghua University and UNSW@ADFA. His research interests are control theory and power system control. Hung T. Nguyen received his BE degree with First Class Honours and University Medal in 1976 and PhD degree in 1980 from the University of Newcastle in Australia. He is currently Assistant Deputy Vice-Chancellor (Innovation), Director of the Centre for Health Technologies, and Professor of Electrical Engineering at UTS. He has been involved with research in the areas of biomedical engineering, artificial intelligence, neurosciences and advanced control for more than 20 years. He was Dean of the Faculty of Engineering and Information Technology at UTS from 2010 to 2014, Founder & CEO/Managing Director of AIMedics Pty Ltd from 2001 to 2006, a recipient of UTS Teaching Award in 2000, and Engineering Manager of Power Electronics Pty Ltd from 1988 to 1990. Branko G. Celler received B.Sc. and B.E. (Hons.) degrees in Electrical Engineering and a Ph.D. degree in Biomedical Engineering from the UNSW, Australia. He is currently a Research Professor at the University of NEW South Wales School of Electrical Engineering. His research interests include biomedical instrumentation and systems, noninvasive modeling of cardiovascular performance, and medical informatics. Prof. Celler was Principal Scientist at the CSIRO Centre for Computational Informatics through to the 31st Dec 2014 and was previously Chief Scientist of the CSIRO ICT Centre. He is a Fellow of the Academy of Technological Sciences and Engineering (ATSE) and an Emeritus Professor at the University of NSW. He has received more than 15m in competitive research funding from sources as varied as the ARC, NH&MRC and DOHA and has published more than 220 refereed conference proceedings, Journal papers and book chapters.