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Other titles in the Advances in Industrial Control series:

Stochastic Distribution Control System Design: A Convex Optimization Approach (Advances in Industrial Control)

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Stochastic Distribution Control System Design: A Convex Optimization Approach (Advances in Industrial Control) Cover

 

Synopses & Reviews

Publisher Comments:

Stochastic distribution control (SDC) systems are widely seen in practical industrial processes, the aim of the controller design being generation of output probability density functions for non-Gaussian systems. Examples of SDC processes are: particle-size-distribution control in chemical engineering, flame-distribution control in energy generation and combustion engines, steel and film production, papermaking and general quality data distribution control for various industries. SDC is different from well-developed forms of stochastic control like minimum-variance and linear-quadratic-Gaussian control, in which the aim is limited to the design of controllers for the output mean and variances. An important recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of linear-matrix-inequality-based (LMI-based) convex optimization methods. Within this theoretical framework, control parameter determination can be designed and stability and robustness of closed-loop systems can be analyzed. Stochastic Distribution Control System Design describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. The book starts with a review of current research on SDC and moves on to some basic techniques for modelling and controller design of SDC systems. This is followed by a description of controller design for fixed-control-structure SDC systems, PDF control for general input- and output-represented systems, filtering designs, and fault detection and diagnosis (FDD) for SDC systems. Many new LMI techniques being developed for SDC systems are shown to have independent theoretical significance for robust control and FDD problems. This monograph will be of interest to academic researchers in statistical, robust and process control, and FDD, process and quality control engineers working in industry and as a reference for graduate control students.

Synopsis:

This book describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. The reader will learn how to expand their use of stochastic control methods.

Synopsis:

A recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of LMI-based convex optimization methods. Within this theoretical framework, control parameter determination can be designed and stability and robustness of closed-loop systems can be analyzed. This book describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. It starts with a review of current research on SDC and moves on to some basic techniques for modelling and controller design of SDC systems. This is followed by a description of controller design for fixed-control-structure SDC systems, PDF control for general input- and output-represented systems, filtering designs, and fault detection and diagnosis (FDD) for SDC systems. Many new LMI techniques being developed for SDC systems are shown to have independent theoretical significance for robust control and FDD problems.

About the Author

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

Table of Contents

Introduction.- Basic Stochastic Distribution Control Systems: Modelling and Controller Design Tools.- PDF Tracking Control with PID Structure for Non-Gaussian Continuous System.- PDF Tracking Control with PID Structure for Non-Gaussian Discrete-time Systems.- Statistic Tracking Control: A Multi-objective Optimization Algorithm.- Optimal Output Probability Density Function Control for Nonlinear ARMAX Stochastic Systems.- FDD for Non-Gaussian Continuous Systems Based on Output PDFs.- Optimal FDD for Non-Gaussian Time-delayed Systems Based on Output PDFs.- Optimal FDD for Non-Gaussian Discrete Systems Based on Output PDFs.- Entropy Optimization Filtering for Fault Isolation of Non-Gaussian Systems.- Conclusions.

Product Details

ISBN:
9781849960298
Author:
Guo, Lei
Publisher:
Springer
Author:
Wang, Hong
Location:
London
Subject:
Automation
Subject:
Chemistry - Industrial & Technical
Subject:
Probability & Statistics - General
Subject:
Control
Subject:
Control Applications
Subject:
control engineering
Subject:
LMI
Subject:
Linear Matrix Inequalities
Subject:
Simulation
Subject:
Stochastic systems.
Subject:
Probability Theory and Stochastic Processes
Subject:
Industrial Chemistry/Chemical Engineering
Subject:
Manufacturing, Machines, Tools
Subject:
Quality Control, Reliability, Safety and Risk
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Subject:
Science Reference-Technology
Subject:
Engineering
Subject:
Language, literature and biography
Subject:
Distribution (Probability theory)
Subject:
Chemical engineering
Subject:
Machinery
Subject:
System safety.
Copyright:
Edition Description:
2010
Series:
Advances in Industrial Control
Publication Date:
20100631
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
214
Dimensions:
235 x 155 mm 1060 gr

Related Subjects

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Engineering » Engineering » General Engineering
Engineering » Industrial and Control Engineering » Control Engineering
Reference » Science Reference » Technology
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Science and Mathematics » Electricity » General Electronics
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Stochastic Distribution Control System Design: A Convex Optimization Approach (Advances in Industrial Control) New Hardcover
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Product details 214 pages Springer - English 9781849960298 Reviews:
"Synopsis" by , This book describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. The reader will learn how to expand their use of stochastic control methods.
"Synopsis" by , A recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of LMI-based convex optimization methods. Within this theoretical framework, control parameter determination can be designed and stability and robustness of closed-loop systems can be analyzed. This book describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. It starts with a review of current research on SDC and moves on to some basic techniques for modelling and controller design of SDC systems. This is followed by a description of controller design for fixed-control-structure SDC systems, PDF control for general input- and output-represented systems, filtering designs, and fault detection and diagnosis (FDD) for SDC systems. Many new LMI techniques being developed for SDC systems are shown to have independent theoretical significance for robust control and FDD problems.
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