Recent Advances In Control And Filtering Of Dynamic Systems With Constrained Signals

This book introduces the principle theories and applications of control and filtering problems to address emerging hot topics in feedback systems. With the development of IT technology at the core of the 4th industrial revolution, dynamic systems are becoming more sophisticated, networked, and advanced to achieve even better performance. However, this evolutionary advance in dynamic systems also leads to unavoidable constraints. In particular, such elements in control systems involve uncertainties, communication/transmission delays, external noise, sensor faults and failures, data packet dropouts, sampling and quantization errors, and switching phenomena, which have serious effects on the system's stability and performance. This book discusses how to deal with such constraints to guarantee the system's design objectives, focusing on real-world dynamical systems such as Markovian jump systems, networked control systems, neural networks, and complex networks, which have recently excited considerable attention. It also provides a number of practical examples to show the applicability of the presented methods and techniques. This book is of interest to graduate students, researchers and professors, as well as R & D engineers involved in control theory and applications looking to analyze dynamical systems with constraints and to synthesize various types of corresponding controllers and filters for optimal performance of feedback systems.

This book introduces the principle theories and applications of control and filtering problems to address emerging hot topics in feedback systems. With the development of IT technology at the core of the 4th industrial revolution, dynamic systems are becoming more sophisticated, networked, and advanced to achieve even better performance. However, this evolutionary advance in dynamic systems also leads to unavoidable constraints. In particular, such elements in control systems involve uncertainties, communication/transmission delays, external noise, sensor faults and failures, data packet dropouts, sampling and quantization errors, and switching phenomena, which have serious effects on the system’s stability and performance. This book discusses how to deal with such constraints to guarantee the system’s design objectives, focusing on real-world dynamical systems such as Markovian jump systems, networked control systems, neural networks, and complex networks, which have recently excited considerable attention. It also provides a number of practical examples to show the applicability of the presented methods and techniques. This book is of interest to graduate students, researchers and professors, as well as R&D engineers involved in control theory and applications looking to analyze dynamical systems with constraints and to synthesize various types of corresponding controllers and filters for optimal performance of feedback systems.

This edited book introduces readers to new analytical techniques and controller design schemes used to solve the emerging “hottest” problems in dynamic control systems and networks. In recent years, the study of dynamic systems and networks has faced major changes and challenges with the rapid advancement of IT technology, accompanied by the 4th Industrial Revolution. Many new factors that now have to be considered, and which haven’t been addressed from control engineering perspectives to date, are naturally emerging as the systems become more complex and networked. The general scope of this book includes the modeling of the system itself and uncertainty elements, examining stability under various criteria, and controller design techniques to achieve specific control objectives in various dynamic systems and networks. In terms of traditional stability matters, this includes the following special issues: finite-time stability and stabilization, consensus/synchronization, fault-tolerant control, event-triggered control, and sampled-data control for classical linear/nonlinear systems, interconnected systems, fractional-order systems, switched systems, neural networks, and complex networks. In terms of introducing graduate students and professional researchers studying control engineering and applied mathematics to the latest research trends in the areas mentioned above, this book offers an excellent guide.

The book focuses on analysis and design for positive stochastic jump systems. By using multiple linear co-positive Lyapunov function method and linear programming technique, a basic theoretical framework is formed toward the issues of analysis and design for positive stochastic jump systems. This is achieved by providing an in-depth study on several major topics such as stability, time delay, finite-time control, observer design, filter design, and fault detection for positive stochastic jump systems. The comprehensive and systematic treatment of positive systems is one of the major features of the book, which is particularly suited for readers who are interested to learn non-negative theory. By reading this book, the reader can obtain the most advanced analysis and design techniques for positive stochastic jump systems.

Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges

The book focuses on control synthesis for semi-Markovian switching systems. By using multiple semi-Markovian Lyapunov function approaches, a basic theoretical framework is formed toward the issue of control synthesis for semi-Markovian switching systems. This is achieved by providing an in-depth study on several major topics such as sliding mode control, finite-time control, quantized control, event-triggered control, synchronization, and fuzzy control for semi-Markovian switching systems. The comprehensive and systematic treatment of semi-Markovian switching systems is one of the major features of the book, which is particularly suitable for readers who are interested to learn control theory and engineering. By reading this book, the reader can obtain the most advanced analysis and design techniques for stochastic switching systems.

Control and Dynamic Systems: Advances in Theory in Applications, Volume 29: Advances in Algorithms and Computational Techniques in Dynamic Systems Control, Part 2 of 3 discusses developments in algorithms and computational techniques for control and dynamic systems. This volume discusses some computational problems which arose in the applications of Kalman filters. It also examines system fault detection techniques; computational techniques in angle-only tracking filtering; development of real-time knowledge of system parameters; and algorithms for decentralized systems with application to stream water quality. This book is an important reference for practitioners in the field who want a comprehensive source of techniques with significant applied implications.

This book presents up-to-date research developments and novel methodologies regarding recursive filtering for 2-D shift-varying systems with various communication constraints. It investigates recursive filter/estimator design and performance analysis by a combination of intensive stochastic analysis, recursive Riccati-like equations, variance-constrained approach, and mathematical induction. Each chapter considers dynamics of the system, subtle design of filter gains, and effects of the communication constraints on filtering performance. Effectiveness of the derived theories and applicability of the developed filtering strategies are illustrated via simulation examples and practical insight. Features:- Covers recent advances of recursive filtering for 2-D shift-varying systems subjected to communication constraints from the engineering perspective. Includes the recursive filter design, resilience operation and performance analysis for the considered 2-D shift-varying systems. Captures the essence of the design for 2-D recursive filters. Develops a series of latest results about the robust Kalman filtering and protocol-based filtering. Analyzes recursive filter design and filtering performance for the considered systems. This book aims at graduate students and researchers in mechanical engineering, industrial engineering, communications networks, applied mathematics, robotics and control systems.