Bounded Dynamic Stochastic Systems: Modelling and Control (Advances in Industrial Control) - Softcover

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Author 1: Professor Lei Guo: 2003-present: Full professor with research activities on stochastic control, nonlinear control, filter design and fault detection, in Institute of Automation, BUAA, Beijing, P R China. He is also affiliated as a full professor with Research Institute of Automation, Southeast University, China. 2002-2003: Research fellow at Dept. Paper Science, UMIST, Manchester, UK. 2001-2002: Research associate in Department of Automobile and Aeronautical Engineering, Loughborough University, UK; 2000-2001: Research associate in Department of Mechanical Engineering, Glasgow University, UK; 1999-2000: Postdoctoral research fellow in IRCCyN, CNRS, Nantes, France, sponsored by Pays de la Loire project. Following Professor Guo's previous work on stochastic distribution control at UMIST, his recent research is mainly focused on the new developments of non-Gaussian filtering algorithms for signal processing and the shape control of stochastic distributions using LMIs. This includes the developments of nonlinear observers and LMI techniques for adaptive tuning rules for nonlinear systems. Professor Lei Guo will (with John Bailleul of Boston University) be general chair of the IEEE Conference on Decision and Control and Chinese Control Conference being held jointly in Shanghai in December 2009. Author 2: Professor Hong Wang: 1982: Received the BSc (first class) degree in Electrical Engineering from Huainan University of Technology, Anhui, P.R. China 1984: Received the MEng (first class) in Automatic Control from Huazhong Univ.Science & Tech, Wuhan, P.R. China 1987: Received the PhD degree in Power Systems Automation from Huazhong Univ. Science & Tech., Wuhan, P.R. China, received an outstanding PhD thesis award and three best papers awards. 2004-present: Professor in Process Control, Director of the Control Systems Centre, School of Electrical and Electronics Engineering, The University of Manchester (formally UMIST), Manchester, working on the control of stochastic distributions for stochastic systems, fault diagnosis and fault tolerant control andcomplex systems modeling. 2002-2003: Professor in Process Control, Control Systems Centre, UMIST, Manchester. 1999-2002: Reader in Process Control at UMIST, working on stochastic distribution control, fault diagnosis and complex systems modeling. 1997-1999: Senior lecturer in Process Control at UMIST, working on stochastic distribution control, fault diagnosis and complex systems modeling. Prof Wang is a fellow of IEE, fellow of InstMC and IEEE Senior Member, and acted as an associate editor for the leading control theory journal (IEEE Transactions on Automatic Control), board member for 4 international journals, and a member of the IFAC Safeprocess Committee, the IFAC Adaptive and Learning Systems Commitee and a member of the IFAC Stochastic Systems Committee. He is the originator of probability density function shape control and has published 190 papers in international journals and conferences (25 invited papers). He is the leading author of 3 books. His research activites have been focused on: i. stochastic distribution control and filtering of general non-Gaussian dynamic stochastic systems and closed-loop entropy minimization; ii. fault detection, diagnosis and fault-tolerant control for dynamic systems; iii. artificial-neural-network-based control systems design and applications to complex systems such as papermaking, combusion systems and systems biology; and iv. plant-wide modeling, fault diagnosis and optimization.

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Over the past decades, although stochastic system control has been studied intensively within the field of control engineering, all the modelling and control strategies developed so far have concentrated on the performance of one or two output properties of the system, such as minimum-variance control or mean-value control. The general assumption used in the formulation of modelling and control strategies is that the distribution of the random signals involved is Gaussian. In this book, a set of new approaches for the control of the output probability density function of stochastic dynamic systems (those subjected to any bounded random inputs), has been developed. In this context, the purpose of control system design becomes the selection of a control signal that makes the shape of the system output's p.d.f. as close as possible to a given distribution. The book contains material on the subjects of:

• Control of single-input single-output and multiple-input multiple-output stochastic systems.

• Stable adaptive control of stochastic distributions.

• Model reference adaptive control.

• Control of nonlinear dynamic stochastic systems.

• Condition monitoring of bounded stochastic distributions.

• Control algorithm design.

• Singular stochastic systems.

A new representation of dynamic stochastic systems is produced by using B-spline functions to describe the output p.d.f.