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.

Control and Dynamic Systems: Advances in Theory and Applications, Volume 21: Nonlinear and Kalman Filtering Techniques, Part 3 of 3 presents the developments in the techniques and technology of the application of nonlinear and Kalman filters. This book provides substantive examples of the methods and technology of the application of Kalman and nonlinear filters. Organized into six chapters, this volume begins with an overview of the unique and relevant treatment of postflight data analysis. This text then examines the control and filter problems for the interception of torpedo-ship situations. Other chapters consider the MLS algorithm, which has been shown to be a superior algorithm in terms of stability and tracking performance when compared to existing least squares batch algorithm that use both a transition matrix and a measurement. The final chapter deals with the significant trends in integrated communication and navigation systems. This book is a valuable resource for mechanical and aerospace engineers.

Volume 19: Nonlinear and Kalman Filtering Techniques, Part 1 of 3 covers the advances in the techniques and technology of nonlinear filters and Kalman filters. The book provides a comprehensive treatment of the theory and applications of nonlinear filters and Kalman filters. Chapters are devoted to the discussion of the exact and approximate state estimation techniques for nonlinear dynamic systems; computational efficiencies in the various approaches to filtering techniques; the implementation of filters by the use of microprocessors and distributed processor systems; and practical means for dealing with nonlinearities. Engineers, economists, seismologists, meteorologists, and communications experts will find this book a good reference material.

​Stochastic Control and Filtering over Constrained Communication Networks presents up-to-date research developments and novel methodologies on stochastic control and filtering for networked systems under constrained communication networks. It provides a framework of optimal controller/filter design, resilient filter design, stability and performance analysis for the systems considered, subject to various kinds of communication constraints, including signal-to-noise constraints, bandwidth constraints, and packet drops. Several techniques are employed to develop the controllers and filters desired, including: recursive Riccati equations; matrix decomposition; optimal estimation theory; and mathematical optimization methods. Readers will benefit from the book’s new concepts, models and methodologies that have practical significance in control engineering and signal processing. Stochastic Control and Filtering over Constrained Communication Networks is a practical research reference for engineers dealing with networked control and filtering problems. It is also of interest to academics and students working in control and communication networks.

Physical, safety and technological constraints suggest that control actuators can neither provide unlimited amplitude signals nor unlimited speed of reaction. The techniques described in this book are useful for industrial applications in aeronautical or space domains, and in the context of biological systems. Such methods are well suited for the development of tools that help engineers to solve analysis and synthesis problems of control systems with input and output constraints.