Yu Recent Advances in Intelligent Control Systems

1. Auflage 2009
ISBN: 978-1-84882-548-2
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 376 Seiten

'Recent Advances in Intelligent Control Systems' gathers contributions from workers around the world and presents them in four categories according to the style of control employed: fuzzy control; neural control; fuzzy neural control; and intelligent control. The contributions illustrate the interdisciplinary antecedents of intelligent control and contrast its results with those of more traditional control methods. A variety of design examples, drawn primarily from robotics and mechatronics but also representing process and production engineering, large civil structures, network flows, and others, provide instances of the application of computational intelligence for control. Presenting state-of-the-art research, this collection will be of benefit to researchers in automatic control, automation, computer science (especially artificial intelligence) and mechatronics while graduate students and practicing control engineers working with intelligent systems will find it a good source of study material.

Autoren/Hrsg.

Yu, Wen

Inhaltsverzeichnis

1;Preface;6
2;Contents;7
3;List of Contributors;13
4;Fuzzy Control;17
4.1;Fuzzy Control of Large Civil Structures Subjected to Natural Hazards;18
4.1.1;1.1 Introduction;19
4.1.2;1.2 Smart Control Device: MR Damper;20
4.1.3;1.3 Background Materials;21
4.1.4;1.4 LMI Formulation of Semiactive Nonlinear Fuzzy Control Systems;24
4.1.5;1.5 Examples;29
4.1.6;1.6 Concluding Remarks;32
4.1.7;References;33
4.2;Approaches to Robust H8 Controller Synthesisof Nonlinear Discrete-time-delay Systems viaTakagi-Sugeno Fuzzy Models;36
4.2.1;2.1 Introduction;36
4.2.2;2.2 Model Description and Robust H8 Piecewise Control Problem;38
4.2.3;2.3 Piecewise H8 Control of T-S Fuzzy Systems with Time-delay;42
4.2.4;2.4 Simulation Examples;55
4.2.5;2.5 Conclusions;61
4.2.6;References;61
4.3;H8 Fuzzy Control for Systems with RepeatedScalar Nonlinearities;65
4.3.1;3.1 Introduction;65
4.3.2;3.2 Problem Formulation;67
4.3.3;3.3 H8 Fuzzy Control Performance Analysis;70
4.3.4;3.4 H8 Fuzzy Controller Design;72
4.3.5;3.5 An Illustrative Example;74
4.3.6;3.6 Conclusions;76
4.3.7;References;78
4.4;Stable Adaptive Compensation with Fuzzy Cerebellar Model Articulation Controller for Overhead Cranes;80
4.4.1;4.1 Introduction;80
4.4.2;4.2 Preliminaries;82
4.4.3;4.3 Control of an Overhead Crane;85
4.4.4;4.4 Position Regulation with FCMAC Compensation;86
4.4.5;4.5 FCMAC Training and Stability Analysis;88
4.4.6;4.6 Experimental Comparisons;92
4.4.7;4.7 Conclusions;97
4.4.8;References;97
5;Neural Control;99
5.1;Estimation and Control of Nonlinear Discrete- time Systems;100
5.1.1;5.1 Background;101
5.1.2;5.2 Estimation of an Unknown Nonlinear Discrete-time System;103
5.1.3;5.3 Neural Network Control Design for Nonlinear Discrete-time Systems;118
5.1.4;5.4 Simulation Results;132
5.1.5;5.5 Conclusions;134
5.1.6;References;134
5.2;Neural Networks Based Probability Density Function Control for Stochastic Systems;136
5.2.1;6.1 Stochastic Distribution Control;136
5.2.2;6.2 Control Input Design for Output PDF Shaping;144
5.2.3;6.3 Introduction of the Grinding Process Control;145
5.2.4;6.4 Model Presentation;146
5.2.5;6.5 System Modeling and Control of Grinding Process;150
5.2.6;6.6 System Simulation and Results;154
5.2.7;6.7 Conclusions;156
5.2.8;References;158
5.3;Hybrid Differential Neural Network Identifier for Partially Uncertain Hybrid Systems;160
5.3.1;7.1 Introduction;160
5.3.2;7.2 Hybrid System;162
5.3.3;7.3 Hybrid DNN Identifier;164
5.3.4;7.4 Examples;167
5.3.5;7.5 Conclusions;174
5.3.6;Appendix;175
5.3.7;References;178
5.4;Real-time Motion Planning of Kinematically Redundant Manipulators Using Recurrent Neural Networks;180
5.4.1;8.1 Introduction;181
5.4.2;8.2 Problem Formulation;182
5.4.3;8.3 Neural Network Models;188
5.4.4;8.4 Simulation Results;192
5.4.5;8.5 Concluding Remarks;201
5.4.6;References;202
5.5;Adaptive Neural Control of Uncertain Multi- variable Nonlinear Systems with Saturation and Dead- zone;205
5.5.1;9.1 Introduction;205
5.5.2;9.2 Problem Formulation and Preliminaries;208
5.5.3;9.3 Adaptive Neural Control and Stability Analysis;211
5.5.4;9.4 Simulation Results;224
5.5.5;9.5 Conclusions;226
5.5.6;Appendix 1;229
5.5.7;Appendix 2;230
5.5.8;References;230
6;Fuzzy Neural Control;233
6.1;An Online Self-constructing Fuzzy Neural Network with Restrictive Growth;234
6.1.1;10.1 Introduction;234
6.1.2;10.2 Architecture of the OSFNNRG;237
6.1.3;10.3 Learning Algorithm of the OSFNNRG;238
6.1.4;10.4 Simulation Studies;243
6.1.5;10.5 Conclusions;254
6.1.6;References;255
6.2;Nonlinear System Control Using Functional- link- based Neuro- fuzzy Networks;257
6.2.1;11.1 Introduction;257
6.2.2;11.2 Structure of Functional-link-based Neuro-fuzzy Network;259
6.2.3;11.3 Learning Algorithms of the FLNFN Model;263
6.2.4;11.4 Simulation Results;268
6.2.5;11.5 Conclusion and Future Works;281
6.2.6;References;282
6.3;An Adaptive Neuro-fuzzy Controller for Robot Navigation;284
6.3.1;12.1 Introduction;284
6.3.2;12.2 The Overall Structure of the Neuro-fuzzy Controller;288
6.3.3;12.3 Design of the Neuro-fuzzy Controller;290
6.3.4;12.4 Simulation Studies;301
6.3.5;12.5 Experiment of Studies;309
6.3.6;12.6 Summary;312
6.3.7;References;313
7;Intelligent Control;315
7.1;Flow Control of Real-time Multimedia Applications in Best- effort Networks;316
7.1.1;13.1 Introduction;316
7.1.2;13.2 Modeling End-to-end Single Flow Dynamics in Best-effort Networks;319
7.1.3;13.3 Proposed Flow Control Strategies;329
7.1.4;13.4 Description of the Network Simulation Scenarios;334
7.1.5;13.5 Voice Quality Measurement Test: E-Model;341
7.1.6;13.6 Validation of Proposed Flow Control Strategies;343
7.1.7;13.7 Summary and Conclusions;356
7.1.8;References;359
7.2;Online Synchronous Policy Iteration Method for Optimal Control;362
7.2.1;14.1 Introduction;362
7.2.2;14.2 The Optimal Control Problem and the Policy Iteration Problem;364
7.2.3;14.3 Online Generalized PI Algorithm with Synchronous Tuning of Actor and Critic Neural Networks;367
7.2.4;14.4 Simulation results;371
7.2.5;14.5 Conclusions;374
7.2.6;Appendix 1;375
7.2.7;Appendix 2;376
7.2.8;References;378
8;Index;380