Muutke küpsiste eelistusi

Advanced Discrete-Time Control: Designs and Applications 2015 ed. [Kõva köide]

,
  • Formaat: Hardback, 224 pages, kõrgus x laius: 235x155 mm, kaal: 4794 g, 122 Illustrations, color; 4 Illustrations, black and white; XII, 224 p. 126 illus., 122 illus. in color., 1 Hardback
  • Sari: Studies in Systems, Decision and Control 23
  • Ilmumisaeg: 07-Apr-2015
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9812874771
  • ISBN-13: 9789812874771
Teised raamatud teemal:
  • Kõva köide
  • Hind: 95,02 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 111,79 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Advanced Discrete-Time Control: Designs and Applications 2015 ed.Suurem pilt
  • Formaat: Hardback, 224 pages, kõrgus x laius: 235x155 mm, kaal: 4794 g, 122 Illustrations, color; 4 Illustrations, black and white; XII, 224 p. 126 illus., 122 illus. in color., 1 Hardback
  • Sari: Studies in Systems, Decision and Control 23
  • Ilmumisaeg: 07-Apr-2015
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9812874771
  • ISBN-13: 9789812874771
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789812874771.html
Märksõnad:
This book covers a wide spectrum of systems such as linear and nonlinear multivariable systems as well as control problems such as disturbance, uncertainty and time-delays. The purpose of this book is to provide researchers and practitioners a manual for the design and application of advanced discrete-time controllers. The book presents six different control approaches depending on the type of system and control problem. The first and second approaches are based on Sliding Mode control (SMC) theory and are intended for linear systems with exogenous disturbances. The third and fourth approaches are based on adaptive control theory and are aimed at linear/nonlinear systems with periodically varying parametric uncertainty or systems with input delay. The fifth approach is based on Iterative learning control (ILC) theory and is aimed at uncertain linear/nonlinear systems with repeatable tasks and the final approach is based on fuzzy logic control (FLC) and is intended for highly uncertain systems with heuristic control knowledge. Detailed numerical examples are provided in each chapter to illustrate the design procedure for each control method. A number of practical control applications are also presented to show the problem solving process and effectiveness with the advanced discrete-time control approaches introduced in this book.

Arvustused

The monograph presents six different control approaches for linear and nonlinear discrete-time systems. The monograph contains some original results of the authors. It is dedicated to scientists and Ph.D students in the field of automatic control systems. (Tadeusz Kaczorek, zbMATH 1314.93003, 2015)

1 Introduction 1(8)
1.1 Background
1(4)
1.2 Contributions
5(2)
1.3 Organization
7(2)
2 Discrete-Thne Sliding Mode Control 9(54)
2.1 Introduction
9(2)
2.2 Problem Formulation
11(4)
2.3 Classical Discrete-Time Sliding Mode Control Revisited
15(6)
2.3.1 State Regulation
15(3)
2.3.2 Output Tracking
18(3)
2.4 Discrete-Time Integral Sliding Mode Control
21(30)
2.4.1 State Regulation with ISM
21(3)
2.4.2 Output-Tracking ISM Control: State Feedback Approach
24(6)
2.4.3 Output Tracking ISM: Output Feedback Approach
30(8)
2.4.4 Output Tracking ISM: State Observer Approach
38(4)
2.4.5 Systems with a Piece-Wise Smooth Disturbance
42(1)
2.4.6 Illustrative Example
43(8)
2.5 Discrete-Time Terminal Sliding Mode Control
51(10)
2.5.1 Controller Design and Stability Analysis
51(4)
2.5.2 TSM Control Tracking Properties
55(1)
2.5.3 Determination of Controller Parameters
56(5)
2.6 Conclusion
61(2)
3 Discrete-Time Periodic Adaptive Control 63(16)
3.1 Introduction
63(1)
3.2 Discrete-Time Periodic Adaptive Control
64(4)
3.2.1 Discrete-Time Adaptive Control Revisited
64(2)
3.2.2 Periodic Adaptation
66(1)
3.2.3 Convergence Analysis
66(2)
3.3 Extension to More General Cases
68(8)
3.3.1 Extension to Multiple Parameters
68(3)
3.3.2 Extension to Mixed Parameters
71(2)
3.3.3 Extension to Tracking Tasks
73(1)
3.3.4 Extension to Higher Order Systems
74(2)
3.4 Illustrative Example
76(2)
3.5 Conclusion
78(1)
4 Discrete-Time Adaptive Posicast Control 79(30)
4.1 Introduction
79(2)
4.2 Problem Formulation
81(1)
4.2.1 Continuous-Time Adaptive Posicast Controller (APC)
82(1)
4.3 Discrete-Time Adaptive Posicast Controller Design
82(11)
4.3.1 Control of a 1st Order Input Time-Delay System in Discrete-Time
83(1)
4.3.2 Adaptive Control of an Input Time-Delay System
84(4)
4.3.3 Extension to Higher Order Systems
88(3)
4.3.4 Stability Analysis
91(2)
4.4 Extension to More General Cases
93(9)
4.4.1 Uncertain Upper-Bounded Time-Delay
93(4)
4.4.2 Extension to Nonlinear Systems
97(5)
4.5 Illustrative Examples
102(4)
4.5.1 Linear Systems
102(3)
4.5.2 Nonlinear Systems
105(1)
4.6 Conclusion
106(3)
5 Discrete-Time Iterative Learning Control 109(36)
5.1 Introduction
109(4)
5.2 Preliminaries
110(1)
5.2.1 Problem Formulation
111(1)
5.2.2 Difference with Continuous-Time Iterative Learning Control
112(1)
5.3 General Iterative Learning Control: Time Domain
113(8)
5.3.1 Convergence Properties
114(2)
5.3.2 D-Type and D2-Type ILC
116(3)
5.3.3 Effect of Time-Delay
119(2)
5.4 General Iterative Learning Control: Frequency Domain
121(6)
5.4.1 Current-Cycle Iterative Learning
122(2)
5.4.2 Considerations for L(q) and Q(q) Selection
124(1)
5.4.3 D-Type and D2-Type ILC
125(2)
5.5 Special Case: Combining ILC with Multirate Technique
127(6)
5.5.1 Controller Design
127(1)
5.5.2 Multirate Structure
127(1)
5.5.3 Iterative Learning Scheme
128(1)
5.5.4 Convergence Condition
129(4)
5.6 Illustrative Example: Time Domain
133(3)
5.6.1 P-Type ILC
133(1)
5.6.2 D-Type and D2-Type ILC
134(2)
5.7 Illustrative Example: Frequency Domain
136(8)
5.7.1 P-Type ILC
136(1)
5.7.2 D-Type and D2-Type ILC
137(1)
5.7.3 Current-Cycle Iterative Learning Control
138(2)
5.7.4 L(q) Selection
140(2)
5.7.5 Sampling Period Selection
142(2)
5.8 Conclusion
144(1)
6 Discrete-Time Fuzzy PID Control 145(20)
6.1 Introduction
145(2)
6.2 Design of Fuzzy PID Control System
147(8)
6.2.1 Fuzzy MD Controller with Parallel Structure
147(5)
6.2.2 Tuning of the Fuzzy PID Controller
152(3)
6.3 Stability and Performance Analysis
155(6)
6.3.1 BIBO Stability Condition of the Fuzzy PID Control System
155(4)
6.3.2 Control Efforts Between Fuzzy and Conventional PID Controllers
159(2)
6.4 Illustrative Example
161(2)
6.5 Conclusion
163(2)
7 Benchmark Precision Control of a Piezo-Motor Driven Linear Stage 165(24)
7.1 Introduction
165(1)
7.2 Model of the Piezo-Motor Driven Linear Motion Stage
166(4)
7.2.1 Overall Model in Continuous-Time
167(1)
7.2.2 Friction Models
167(2)
7.2.3 Overall Model in Discrete-Time
169(1)
7.3 Discrete-Time Output ISM Control
170(13)
7.3.1 Controller Design and Stability Analysis
171(2)
7.3.2 Disturbance Observer Design
173(2)
7.3.3 State Observer Design
175(1)
7.3.4 Ultimate Tracking Error Bound
176(2)
7.3.5 Experimental Investigation
178(5)
7.4 Discrete-Time Terminal Sliding Mode Control
183(1)
7.5 Sampled-Data ILC Design
184(3)
7.5.1 Controller Parameter Design and Experimental Results
184(3)
7.6 Conclusion
187(2)
8 Advanced Control for Practical Engineering Applications 189(26)
8.1 Introduction
189(1)
8.2 Periodic Adaptive Control of a PM Synchronous Motor
190(5)
8.2.1 Problem Definition
190(1)
8.2.2 Control Strategy and Results
191(4)
8.3 Multirate ILC of a Ball and Beam System
195(5)
8.3.1 System Model
195(1)
8.3.2 Target Trajectory
196(1)
8.3.3 Controller Configurations
197(1)
8.3.4 System Verifications
197(3)
8.4 Discrete-Time Fuzzy PID of a Coupled Tank System
200(2)
8.4.1 System Description
201(1)
8.4.2 Experiment
201(1)
8.5 Iterative Learning Control for Freeway Traffic Control
202(11)
8.5.1 Traffic Model and Analysis
203(4)
8.5.2 Density Control
207(3)
8.5.3 Flow Control
210(3)
8.6 Conclusion
213(2)
Appendix: Derivation of BIBO Stability Condition of Linear PID Control System 215(2)
References 217
Professor Jian-Xin Xu received the Bachelor degree from Zhejiang University, China in 1982. He attended the University of Tokyo, Japan, where he received Master's and PhD degrees in 1986 and 1989 respectively. All degrees are in Electrical Engineering. He worked for one year in the Hitachi research Laboratory, Japan; for more than one year in Ohio State University, U.S.A. as a Visiting Scholar; and for 6 months in Yale University as a Visiting Research Fellow. In 1991 Professor Xu joined the National University of Singapore and is currently a professor at Department of Electrical and Computer Engineering. His research interests lie in the fields of intelligent and robust control and applications to motion control, mechatronics, and robotics. He is a Fellow of IEEE.

Up to now he produced more than 500 peer-reviewed journal and conference papers, 2 monographs and 3 edited books. He has been supervising/co-supervising 29 PhD, 20 Master students, and 15 research staff including postdoctoral fellows and research fellows. He has completed 20 funded research projects and currently he work on AUV biomimetic locomotion and control.

Dr Khalid Abidi received his BSc. degree in Mechanical Engineering from the Middle East Technical University, Ankara, Turkey in 2002 and the MSc. degree in Electrical Engineering and Computer Science from Sabanci University, Istanbul, Turkey in 2004. He obtained his PhD degree in Electrical and Computer Engineering, specializing in the area o

f Control Engineering, from the National University of Singapore in 2009. Dr Abidi is currently a lecturer of Electrical Power Engineering at Newcastle University based in Singapore. Prior to joining Newcastle University Dr Abidi worked as an Assistant Professor of Mechatronics Engineering at Bahcesehir University, Istanbul, Turkey from September 2009 until June 2014. His research interests include: Theory and modelling of dynamical systems, Discrete-Time systems, Time-delay systems, Learning Control, Robust Control, Applied Nonlinear Control, Robotics and Mechatronic Systems.