Control And Filtering Of Fuzzy Systems Under Communication Channels
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Control and Filtering of Fuzzy Systems Under Communication Channels
This book systematically studies the feedback control and filtering problems for nonlinear plants with limited communication channels based on T-S fuzzy models. By fully considering different network-induced phenomena, such as signal quantizations, time-delays, data packet dropouts, communication protocols, cyber attacks, and so on, some significant strategies are provided for various performance analysis and different controller/filter synthesis of fuzzy systems. The event-triggered mechanism is also mentioned to save the communication resource. Moreover, some results are extended to the fault detection and fault-tolerant control. The book provides some new methodologies in analysis and synthesis of fuzzy systems under communication channels, and can serve as a valuable reference material for researchers wishing to explore the area of control and filtering of fuzzy systems and networked systems.
Control and Filtering of Fuzzy Systems with Switched Parameters
This book presents recent advances in control and filter design for Takagi-Sugeno (T-S) fuzzy systems with switched parameters. Thanks to its powerful ability in transforming complicated nonlinear systems into a set of linear subsystems, the T-S fuzzy model has received considerable attention from those the field of control science and engineering. Typical applications of T-S fuzzy systems include communication networks, and mechanical and power electronics systems. Practical systems often experience abrupt variations in their parameters or structures due to outside disturbances or component failures, and random switching mechanisms have been used to model these stochastic changes, such as the Markov jump principle. There are three general types of controller/filter for fuzzy Markov jump systems: mode-independent, mode-dependent and asynchronous. Mode-independence does not focus on whether modes are accessible and ignores partially useful mode information, which results in some conservatism. The mode-dependent design approach relies on timely, complete and correct information regarding the mode of the studied plant. Factors like component failures and data dropouts often make it difficult to obtain exact mode messages, which further make the mode-dependent controllers/filters less useful. Recently, to overcome these issues, researchers have focused on asynchronous techniques. Asynchronous modes are accessed by observing the original systems based on certain probabilities. The book investigates the problems associated with controller/filter design for all three types. It also considers various networked constraints, such as data dropouts and time delays, and analyzes the performances of the systems based on Lyapunov function and matrix inequality techniques, including the stochastic stability, dissipativity, and $H_\infty$. The book not only shows how these approaches solve the control and filtering problems effectively, but also offers potential meaningful research directions and ideas. Covering a variety of fields, including continuous-time and discrete-time Markov processes, fuzzy systems, robust control, and filter design problems, the book is primarily intended for researchers in system and control theory, and is also a valuable reference resource for graduate and undergraduate students. Further, it provides cases of fuzzy control problems that are of interest to scientists, engineers and researchers in the field of intelligent control. Lastly it is useful for advanced courses focusing on fuzzy modeling, analysis, and control.
Intelligent Control, Filtering and Model Reduction Analysis for Fuzzy-Model-Based Systems
This book aims to introduce the state-of-the-art research of stability/performance analysis and optimal synthesis methods for fuzzy-model-based systems. A series of problems are solved with new approaches of design, analysis and synthesis of fuzzy systems, including stabilization control and stability analysis, dynamic output feedback control, fault detection filter design, and reduced-order model approximation. Some efficient techniques, such as Lyapunov stability theory, linear matrix inequality, reciprocally convex approach, and cone complementary linearization method, are utilized in the approaches. This book is a comprehensive reference for researchers and practitioners working on intelligent control, model reduction, and fault detection of fuzzy systems, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts and methodologies with theoretical and practical significance in system analysis and control synthesis.