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Robust Control for Discrete-Time Markovian Jump Systems in the Finite-Time Domain

  • Book
  • © 2023

Overview

  • Provides a comprehensive guide to finite-time performance of multi-model systems
  • Offers the tools for the analysis and design of industrial processes
  • Includes examples and simulations to demonstrate the engineering potential of the models discussed

Part of the book series: Lecture Notes in Control and Information Sciences (LNCIS, volume 492)

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Table of contents (11 chapters)

Keywords

About this book

This book provides robust analysis and synthesis tools for Markovian jump systems in the finite-time domain with specified performances. It explores how these tools can make the systems more applicable to fields such as economic systems, ecological systems and solar thermal central receivers, by limiting system trajectories in the desired bound in a given time interval.

Robust Control for Discrete-Time Markovian Jump Systems in the Finite-Time Domain focuses on multiple aspects of finite-time stability and control, including:

  • finite-time H-infinity control;
  • finite-time sliding mode control;
  • finite-time multi-frequency control;
  • finite-time model predictive control; and
  • high-order moment finite-time control for multi-mode systems

and also provides many methods and algorithms to solve problems related to Markovian jump systems with simulation examples that illustrate the design procedure and confirm the results of the methods proposed.

The thorough discussion of these topics makes the book a useful guide for researchers, industrial engineers and graduate students alike, enabling them systematically to establish the modeling, analysis and synthesis for Markovian jump systems in the finite-time domain.

Authors and Affiliations

  • Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, China

    Xiaoli Luan, Fei Liu

  • Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education), School of Electrical Engineering and Automation, Anhui University, Hefei, China

    Shuping He

About the authors

Professor Xiaoli Luan received her BSc in automation and PhD in control theory and control engineering from Jiangnan University in 2002, and 2010, respectively. She is now a Professor in the Institute of Automation at the same university. Her research interests are focused on modeling, control and optimization for complex industrial processes. She has authored or coauthored more than 100 articles in professional journals, conference proceedings, and technical reports.

Professor Shuping He received his B.S. degree in automation and his Ph.D. degree in control theory and control engineering from Jiangnan University, in 2005 and 2011, respectively. From 2010 to 2011, he was a Visiting Scholar with the Control Systems Centre, at the University of Manchester, and from 2011 to 2013 he was a Senior Lecturer with Anhui University, before becoming a Professor there. His current research interests include control theory & control systems, systems modeling, methods and applications, signal processing and artificial intelligence. He has coauthored a book about stochastic systems and authored or coauthored over authored over 90 papers in professional journals, conference proceedings, and technical reports.


Professor Fei Liu received his BSc and MSc degrees from the Wuxi Institute of Light Industry in 1987 and 1990 respectively, and his PhD from Zhejiang University in 2002. He has had several academic and teaching positions, including his current role of Professor at the Institute of Automation of Jiangnan University. His research interests include advanced control theory, state estimation, industrial process monitoring and optimization. He’s authored or co-authored numerous journal articles and conference papers

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