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 and 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 outputs 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 descripe the output p.d.f. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Broaches new ground in considering a wide range of system output distributions Fault detection methods applicable in diverse control situations..

Autorentext

Yechen Qin is currently a Postdoctoral Fellow of mechanical engineering at the Beijing Institute of Technology, where he received his B. Eng and Ph.D. in 2010 and 2016, respectively. From 2013-2014, he studied at Texas A&M University as a visiting Ph.D. student. From 2017-2018, he studied at the University of Waterloo as a visiting scholar. His research interests include vehicle dynamics control, road estimation, and in-wheel motor vibration control.Hong Wang is currently a research associate of Mechanical and Mechatronics Engineering at the University of Waterloo. She received her Ph.D. from the Beijing Institute of Technology in China in 2015. Her research focuses on component sizing, modeling of hybrid powertrains, and energy management control strategies design for hybrid electric vehicles; intelligent control theory and application; and autonomous vehicles.Yanjun Huang is a Postdoctoral Fellow at the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, where he received his Ph.D. in 2016. His research interest is primarily focused on the vehicle holistic control in terms of safety, energy-saving, and intelligence, including vehicle dynamics and control, HEV/EV optimization and control,motion planning and control of connected and autonomous vehicles, and human-machine cooperative driving. He has published several books and over 50 papers in journals and conferences. He currently serves as an associate editor and editorial board member of IET Intelligent Transport System, SAE International Journal of Commercial Vehicles, International Journal of Vehicle Information and Communications, Automotive Innovation, AIME, etc.Xiaolin Tang received a B.S. in mechanics engineering and an M.S. in vehicle engineering from Chongqing University, China, in 2006 and 2009, respectively. He received a Ph.D. in mechanical engineering from Shanghai Jiao Tong University, China, in 2015. He is currently an Associate Professor at theState Key Laboratory of Mechanical Transmissions and at the Department of Automotive Engineering, Chongqing University,Chongqing, China. Heis also acommitteemanof Technical Committee on Vehicle Control and Intelligence of Chinese Association of Automation (CAA). He has led and has been involved in more than 10 research projects, such as National Natural Science Foundation of China, and has published more than 20 papers. His research focuses on Hybrid Electric Vehicles (HEVs), vehicle dynamics, noise and vibration, and transmission control.

Inhalt
1 Preliminaries.- 1.1 Introduction.- 1.2 An example: flocculation model.- 1.3 The aim of the new development.- 1.4 The structure of the book.- 1.5 Random variables and stochastic processes.- 1.6 Stochastic processes.- 1.7 Some typical distributions.- 1.8 Conclusions.- 2 Control of SISO Stochastic Systems: A Fundamental Control Law.- 2.1 Introduction.- 2.2 Preliminaries on B-splines artificial neural networks.- 2.3 Model representation.- 2.4 System modelling and parameter estimation.- 2.5 Control algorithm design.- 2.6 Discussions.- 2.7 Examples.- 2.8 Conclusions.- 3 Control of MIMO Stochastic Systems: Robustness and Stability.- 3.1 Introductionx.- 3.2 Model representation.- 3.3 The controller using V(k).- 3.4 The controller using f(y, U(k)).- 3.5 An illustrative example.- 3.6 Conclusions and discussions.- 4 Realization of Perfect Tracking.- 4.1 Introduction.- 4.2 Preliminaries and model representation.- 4.3 Main result.- 4.4 Simulation results.- 4.5 An LQR based algorithm.- 4.6 Conclusions.- 5 Stable Adaptive Control of Stochastic Distributions.- 5.1 Introduction.- 5.2 Model representation.- 5.3 On-line estimation and its convergence.- 5.4 Adaptive control algorithm design.- 5.5 Stability analysis.- 5.6 A simulated example.- 5.7 Conclusions.- 6 Model Reference Adaptive Control.- 6.1 Introduction.- 6.2 Model representation.- 6.3 An adaptive controller design.- 6.4 Adaptive tuning rules for K(t) and Q(t).- 6.5 Robust adaptive control scheme.- 6.6 A case study.- 6.7 Conclusions and discussions.- 7 Control of Nonlinear Stochastic Systems.- 7.1 Introduction.- 7.2 Model representation.- 7.3 Control algorithm design.- 7.4 Stability issues.- 7.5 A neural network approach.- 7.6 Two examples.- 7.7 Calculation of ?.- 7.8 Conclusions.- 8 Application to Fault Detection.- 8.1Introduction.- 8.2 Model representation.- 8.3 Fault detection.- 8.4 An adaptive diagnostic observer.- 8.5 Discussions.- 8.6 An identification based FDD.- 8.7 Fault diagnosis.- 8.8 Discussions and conclusions.- 9 Advanced Topics.- 9.1 Introduction.- 9.2 Square root models.- 9.3 Control algorithm design.- 9.4 Simulations.- 9.5 Continuous-time models.- 9.6 The control algorithm.- 9.7 Control of the mean and variance.- 9.8 Singular stochastic systems.- 9.9 Pseudo ARMAX systems.- 9.10 Filtering issues.- 9.11 Conclusions.- References.