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Real-time Iterative Learning Control: Design and Applications 2009 ed. [Kõva köide]

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  • Formaat: Hardback, 194 pages, kõrgus x laius: 235x155 mm, kaal: 1050 g, XVI, 194 p., 1 Hardback
  • Sari: Advances in Industrial Control
  • Ilmumisaeg: 23-Dec-2008
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1848821743
  • ISBN-13: 9781848821743
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Real-time Iterative Learning Control: Design and Applications 2009 ed.Suurem pilt
  • Formaat: Hardback, 194 pages, kõrgus x laius: 235x155 mm, kaal: 1050 g, XVI, 194 p., 1 Hardback
  • Sari: Advances in Industrial Control
  • Ilmumisaeg: 23-Dec-2008
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1848821743
  • ISBN-13: 9781848821743
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781848821743.html
Märksõnad:
Real-time Iterative Learning Control demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in linear and nonlinear plants pervading mechatronics and batch processes are addressed, in particular: ILC design in the continuous- and discrete-time domains; design in the frequency and time domains; design with problem-specific performance objectives including robustness and optimality; design in a modular approach by integration with other control techniques; and design by means of classical tools based on Bode plots and state space.

This book demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and is a source of illustrative case studies.
1 Introduction 1
2 Introduction to ILC: Concepts, Schematics, and Implementation 7
2.1 ILC for Linear Systems
7
2.1.1 Why ILC?
7
2.1.2 Previous Cycle Learning
8
2.1.3 Current Cycle Learning
10
2.1.4 Previous and Current Cycle Learning
11
2.1.5 Cascade ILC
12
2.1.6 Incremental Cascade ILC
14
2.2 ILC for Non-linear Systems
16
2.2.1 Global Lipschitz Continuity Condition
17
2.2.2 Identical Initialization Condition
19
2.3 Implementation Issues
22
2.3.1 Repetitive Control Tasks
22
2.3.2 Robustness and Filter Design
23
2.3.3 Sampled-data ILC
25
2.4 Conclusion
27
3 Robust Optimal ILC Design for Precision Servo: Application to an XY Table 29
3.1 Introduction
29
3.2 Modelling and Optimal Indices
32
3.2.1 Experimental Setup and Modelling
32
3.2.2 Objective Functions for Sampled-data ILC Servomechanism
33
3.3 Optimal PCL Design
35
3.4 Optimal CCL Design
37
3.5 Optimal PCCL Design
40
3.6 Robust Optimal PCCL Design
42
3.7 Conclusion
44
4 ILC for Precision Servo with Input Non-linearities: Application to a Piezo Actuator 47
4.1 Introduction
47
4.2 ILC with Input Deadzone
50
4.3 ILC with Input Saturation
53
4.4 ILC with Input Backlash
54
4.5 ILC Implementation on Piezoelectric Motor with Input Deadzone
55
4.5.1 PI Control Performance
58
4.5.2 ILC Performance
58
4.6 Conclusion
62
5 ILC for Process Temperature Control: Application to a Water-heating Plant 65
5.1 Introduction
65
5.2 Modelling the Water-heating Plant
67
5.3 Filter-based ILC
72
5.3.1 The Schematic of Filter-based ILC
72
5.3.2 Frequency-domain Convergence Analysis of Filter-based ILC
73
5.4 Temperature Control of the Water-heating Plant
76
5.4.1 Experimental Setup
76
5.4.2 Design of ILC Parameters M and γ
76
5.4.3 Filter-based ILC Results for &gamma = 0.5 and M = 100
78
5.4.4 Profile Segmentation with Feedforward Initialization
78
5.4.5 Initial Re-setting Condition
80
5.5 Conclusion
82
5.6 Appendix: The Physical Model of the Water-heating Plant
82
6 ILC with Robust Smith Compensator: Application to a Furnace Reactor 85
6.1 Introduction
85
6.2 System Description
86
6.3 ILC Algorithms with Smith Time-delay Compensator
88
6.4 ILC with Prior Knowledge of the Process
91
6.4.1 ILC with Accurate Transfer Function (P0 = P0)
91
6.4.2 ILC with Known Upper Bound of the Time Delay
94
6.5 Illustrative Examples
95
6.5.1 Simulation Studies
95
6.5.2 Experiment of Temperature Control on a Batch Reactor..
97
6.6 Conclusion
98
7 Plug-in ILC Design for Electrical Drives: Application to a PM Synchronous Motor 101
7.1 Introduction
101
7.2 PMSM Model
103
7.3 Analysis of Torque Pulsations
104
7.4 ILC Algorithms for PMSM
106
7.4.1 ILC Controller Implemented in Time Domain
107
7.4.2 ILC Controller Implemented in Frequency Domain
108
7.5 Implementation of Drive System
110
7.6 Experimental Results and Discussions
112
7.6.1 Experimental Results
112
7.6.2 Torque Pulsations Induced by the Load
116
7.7 Conclusion
120
8 ILC for Electrical Drives: Application to a Switched Reluctance Motor 121
8.1 Introduction
121
8.2 Review of Earlier Studies
124
8.3 Cascaded Torque Controller
124
8.3.1 The TSF
125
8.3.2 Proposed Torque to Current Conversion Scheme
126
8.3.3 ILC-based Current Controller
128
8.3.4 Analytical Torque Estimator
130
8.4 Experimental Validation of the Proposed Torque Controller
132
8.5 Conclusion
135
9 Optimal Tuning of PID Controllers Using Iterative Learning Approach 141
9.1 Introduction
141
9.2 Formulation of PID Auto-tuning Problem
144
9.2.1 PID Auto-tuning
144
9.2.2 Performance Requirements and Objective Functions
145
9.2.3 A Second-order Example
145
9.3 Iterative Learning Approach
148
9.3.1 Principal Idea of Iterative Learning
148
9.3.2 Learning Gain Design Based on Gradient Information
150
9.3.3 Iterative Searching Methods
153
9.4 Comparative Studies on Benchmark Examples
154
9.4.1 Comparisons Between Objective Functions
155
9.4.2 Comparisons Between ILT and Existing Iterative Tuning Methods
156
9.4.3 Comparisons Between ILT and Existing Auto-tuning Methods
157
9.4.4 Comparisons Between Search Methods
158
9.4.5 ILT for Sampled-data Systems
160
9.5 Real-time Implementation
161
9.5.1 Experimental Setup and Plant Modelling
161
9.5.2 Application of ILT Method
162
9.5.3 Experimental Results
163
9.6 Conclusion
163
9.7 Appendix
164
9.7.1 Underdamped Case
164
9.7.2 Overdamped Case
165
9.7.3 Critical-damped Case
166
10 Calibration of Micro-robot Inverse Kinematics Using Iterative Learning Approach 169
10.1 Introduction
169
10.2 Basic Idea of Iterative Learning
171
10.3 Formulation of Iterative Identifications
171
10.4 Robustness Analysis with Calibration Error
175
10.5 Example
176
10.5.1 Estimation with Accurate Calibration Sample
177
10.5.2 Estimation with Single Imperfect Factor in Calibration Sample
178
10.5.3 Estimation with Multiple Imperfect Factors in Calibration Sample
179
10.6 Conclusion
180
11 Conclusion 181
References 183
Index 191