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2019 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals

verfasst von: Prof. Ju H. Park, Prof. Hao Shen, Xiao-Heng Chang, Prof. Tae H. Lee

Verlag: Springer International Publishing

Buchreihe : Studies in Systems, Decision and Control

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
During the past two decades, since the advanced IT technology including high-speed communication networks has been rapidly developed, some control problems of dynamic systems or networks with some system constraints have widely studied and attracted much attention by many researchers. It is well known that the factors including communication/transmission delays, internal uncertainties, external noises, sensor faults, sampling and quantization errors, and switching phenomena in system mode which are common in the system deeply give serious effect to system’s stability and performance.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee

Control Problems

Frontmatter
Chapter 2. Network-Based Control with Asynchronous Samplings and Quantizations
Abstract
In this chapter, a problem of the network-based control system consisted of the continuous-time plant and controller is addressed. In both transmission and receiving channels, asynchronous samplings and different logarithmic quantization effects are considered. The quantizer is assumed to be satisfied by the sector bounded condition and it is handled by the convex combination technique. To deal with sampling effect, we categorize three cases: one synchronous sampling case and two asynchronous sampling cases in which novel models for sampled-data signals are proposed. Finally, sufficient conditions of the existence of desired controllers for every three cases are presented in the form of linear matrix inequalities (LMIs) and a numerical example is given to illustrate the validity of the proposed methods.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Chapter 3. Quantized Static Output Feedback Control for Discrete-Time Systems
Abstract
This chapter investigates the problem of robust static output feedback control for uncertain discrete-time systems subject to the effects of dynamic quantization in the communication channels from the sensor to the controller and from the controller to the actuator. In the presence of the input and output dynamic quantization effects, the attention of this chapter is focused on the design of both robust static output feedback stabilization and \(\mathscr {H}_\infty \) controllers to asymptotically stabilize the closed-loop systems or achieve the prescribed \(\mathscr {H}_\infty \) performance. The sufficient conditions for the existence of such output feedback robust controllers are proposed in the form of linear matrix inequalities (LMIs), which can be easily solved with the help of Matlab. Finally, a numerical example is given to show the effectiveness of the proposed design method.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Chapter 4. Sampled-Data Control for a Class of Linear Systems with Randomly Occurring Missing Data
Abstract
In this chapter, design methods of the sampled-data control under missing data for dynamics systems have been developed. Two models of discontinuous sampled-data control signals in the presence of the consecutive missing data scenario are proposed by using stochastic variables with a Bernoulli distributed white sequence. Sufficient conditions for the existence of desired sampled-data controllers are presented in the form of a linear matrix inequality. Finally, developed methods have been applied to RLC circuit to show the validity of the proposed methods.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Chapter 5. Reliable Event-Triggered Retarded Dynamic Output Feedback Control for Networked Systems
Abstract
This chapter focuses on the problem of reliable event-triggered \(\mathscr {H}_{\infty }\) control for networked control systems by using retarded dynamic output feedback. The randomness of actuators failures is modeled by a stochastic variable in a Markov jump model framework. In this paper, a Markov jump event-triggered retarded dynamic output feedback \(\mathscr {H}_{\infty }\) controller is designed to guarantee the considered closed-loop system is stochastically stable with a prescribed \(\mathscr {H}_{\infty }\) performance level. According to the stochastic analysis techniques and novel integral inequalities, some sufficient conditions for the solvability of the addressed problem are derived. Finally, an example using a satellite control system model is provided to explain the validity of the proposed method.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Chapter 6. Reliable Event-Triggered Control for Markov Jump Systems
Abstract
This chapter studies the problem of the reliable \(\mathscr {H}_{\infty }\) control for Markov jump systems by using an event-triggered sampling information scheme. The event-triggered mechanism is taken into account to save the limited communication bandwidth. Furthermore, the fault-tolerance and \(\mathscr {H}_{\infty }\) performance are also considered in designing a controller which ensures that the resulting closed-loop system is asymptotically stable and simultaneously satisfies an \(\mathscr {H}_{\infty }\) property in the presence of the actuator failures. A numerical example is given to show the effectiveness of the proposed design scheme
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee

Filtering Problems

Frontmatter
Chapter 7. Fuzzy Resilient Energy-to-Peak Filter Design for Continuous-Time Nonlinear Systems
Abstract
This chapter deals with the problem of resilient energy-to-peak filtering for a class of uncertain continuous-time nonlinear systems. To describe the nonlinear systems, the Takagi-Sugeno (T-S) fuzzy model with norm-bounded uncertainties is employed. By using a two-step approach, two new sufficient design conditions for the resilient filter are presented in terms of solutions to a set of linear matrix inequalities (LMIs), which guarantee the asymptotical stability and the prescribed energy-to-peak performance of the filtering error system. In contrast to the existing results for resilient energy-to-peak filter design, this chapter is toward all filter matrices with gain variations and improves the existing results. An example is provided to demonstrate the rationality of the theoretical results.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Chapter 8. Fuzzy Generalized Filtering for Nonlinear Discrete-Time Systems With Measurement Quantization
Abstract
This chapter investigates the problem of robust generalized \(\mathscr {H}_2\) filtering for uncertain nonlinear systems with the effects of dynamic quantization in the communication channel from the sensor to the filter based on T-S fuzzy model method. In the presence of dynamic quantization, we aim to design both full- and reduced-order generalized \(\mathscr {H}_2\) filters to asymptotically stabilize the filtering error systems and achieve generalized \(\mathscr {H}_2\) performances. In contrast with some published papers on the filtering design with dynamic quantization, the obtained design conditions are based on linear matrix inequalities (LMIs) which can be easily solved with the help of Matlab. Finally, a numerical example will be used to show the obtained design approaches of generalized \(\mathscr {H}_2\) filtering with quantization are effective.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Chapter 9. Event-Triggered Dissipative Filtering for Networked Semi-Markov Jump Systems
Abstract
The event-triggered dissipative filtering problem for a class of networked semi-Markov jump systems is studied in this chapter. For saving the limited network bandwidth and preserving the fixed system performance, the event-triggered communication scheme is introduced. By means of stochastic analysis, the information on the sojourn-time between mode jumps of the underlying systems is fully taken into account. Through using the method of time delay, this paper analyses the filtering performance of the system, and combining with the synergy of event-triggered mechanism and dissipative filter design, completed the filtering error system strictly dissipative. Finally, taking the model of mass spring system as an example, the applicability of this filtering scheme is illustrated.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee

Application Problems

Frontmatter
Chapter 10. Network-Based State Estimation for Neural Networks Using Limited Measurement
Abstract
This chapter is concerned with the network-based \(\mathscr {H}_{\infty }\) state estimation problem for neural networks. Because of network constraints, we consider that transmitted measurements suffer from the sampling effect, external disturbance, network-induced delay, and packet dropout, simultaneously. The external disturbance, network-induced delay, and packet dropout affect the measurements at only the sampling instants owing to the sampling effect. In addition, when packet dropout occurs, the last received data are used. To overcome the difficulty in estimating original signals from the limited signals, a compensator is designed. By aid of the compensator, a state estimator designed which guarantees desired \(\mathscr {H}_{\infty }\) performance. A numerical example is given to illustrate the validity of the proposed methods.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Chapter 11. Mixed Passive Synchronization for Complex Dynamical Networks with Sampled-Data Control
Abstract
In this chapter, the mixed \(\mathscr {H}_{\infty }/\)passive synchronization for complex dynamical networks with time-varying delays via a sampled-data control is investigated. The main objective is to synthesize a controller such that the closed-loop system is exponentially stable and satisfies a mixed \(\mathscr {H}_{\infty }/\)passive performance. A delay-dependent Lyapunov functional combined with some novel integral inequalities is employed to reduce the conservatism. In this case, a sufficient condition, which guarantees the existence of the desired controller is presented. Finally, an example is given to show the effectiveness and improvement of the proposed scheme.
Ju H. Park, Hao Shen, Xiao-Heng Chang, Tae H. Lee
Backmatter
Metadaten
Titel
Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals
verfasst von
Prof. Ju H. Park
Prof. Hao Shen
Xiao-Heng Chang
Prof. Tae H. Lee
Copyright-Jahr
2019
Electronic ISBN
978-3-319-96202-3
Print ISBN
978-3-319-96201-6
DOI
https://doi.org/10.1007/978-3-319-96202-3

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