Schlagwort(e):
Intelligent control systems -- Mathematical models.
;
Automatic control -- Mathematical models.
;
Artificial intelligence -- Engineering applications.
;
Electronic books.
Beschreibung / Inhaltsverzeichnis:
This book examines iterative learning control (ILC) with a focus on design and implementation. It presents a framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between performance and stability.
Materialart:
Online-Ressource
Seiten:
1 online resource (232 pages)
Ausgabe:
1st ed.
ISBN:
9789814585606
Serie:
Advances in Industrial Control Series
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=1783831
DDC:
629.8
Sprache:
Englisch
Anmerkung:
Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Background -- 1.1.1 What Is ILC? -- 1.1.2 A Brief History -- 1.2 Basics of ILC -- 1.2.1 ILC Formulation -- 1.2.2 Comparison of ILC in Different Domains -- 1.3 ILC Design and Analysis -- 1.3.1 ILC Learning Laws -- 1.3.2 Two ILC Configurations -- 1.3.3 Convergence Analysis -- 1.3.4 Transient Analysis -- 1.4 Robotic System with ILC -- 1.5 About the Book -- References -- 2 Learnable Band Extension and Multi-channel Configuration -- 2.1 A-Type Learning Control -- 2.2 Convergence Analysis of A-Type ILC -- 2.3 Design of A-Type ILC -- 2.3.1 Lead-Time Selection -- 2.3.2 Gain Selection -- 2.3.3 Robustness in Design -- 2.4 A Design Example of A-Type ILC -- 2.4.1 Learning Control Design -- 2.4.2 Comparison of D-, P-, PD-, and A-Type ILCs -- 2.4.3 Case Study and Experiments -- 2.5 A-Type ILC Based Multiple Channel Learning -- 2.5.1 Multi-channel Structure for ILC -- 2.5.2 Error Separation -- 2.6 Multi-channel A-Type ILC -- 2.7 Design of Multi-channel A-Type ILC -- 2.8 Robot Application of Multi-channel A-Type ILCs -- 2.9 Conclusion -- References -- 3 Learnable Bandwidth Extension by Auto-Tunings -- 3.1 Cutoff Frequency Tuning -- 3.1.1 Objective and Problems -- 3.1.2 Learning Stability -- 3.1.3 Learning Divergence -- 3.1.4 Cutoff Frequency Tuning -- 3.1.5 Termination of Tuning -- 3.2 Lead Step Tuning -- 3.2.1 Basis of Tuning -- 3.2.2 Tuning Method -- 3.3 Experiment on Auto-Tuning ILC -- 3.3.1 Experiment 1: A-Type ILC with l=5 and γ=1 -- 3.3.2 Experiment 2: One-Step-Ahead ILC with l=1 and γ=1 -- 3.3.3 Experiment 3: Tuning Lead Step with γ=1 -- 3.4 Conclusion -- References -- 4 Reverse Time Filtering Based ILC -- 4.1 Best Phase Lead and Generation Method for SISO ILC System -- 4.2 Learning Control Using Reversed Time Input Runs -- 4.2.1 Learning Law -- 4.2.2 Model Based Approach.
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4.3 Comparison with Other Works -- 4.4 Case Study of Robot Application -- 4.4.1 Exact Zero Phase -- 4.4.2 Reverse Time Filtering Using a Model -- 4.4.3 Robot Performance and Experiments -- 4.5 MIMO ILC System and Error Contraction -- 4.6 Clean System Inversion ILC -- 4.7 System Hermitian ILC -- 4.8 An Example of Robot Joints and Experiments -- 4.9 Conclusion -- References -- 5 Wavelet Transform Based Frequency Tuning ILC -- [DELETE] -- 5.1 Wavelet Packet Algorithm for Error Analysis -- 5.1.1 Wavelet Packet Algorithm -- 5.1.2 Error Analysis Using Wavelet Packet Algorithm -- 5.2 Cutoff Frequency Tuning ILC -- 5.2.1 Cutoff Frequency Tuning Scheme -- 5.2.2 Design of Zero-Phase Low-Pass Filter -- 5.3 Time-Frequency Domain Analysis -- 5.4 Case Study of Frequency Tuning ILC -- 5.4.1 Determination of Learning Gain -- 5.4.2 Determination of Lead Step -- 5.4.3 Determination of Decomposition Level -- 5.4.4 Experimental Results -- 5.5 Conclusion -- References -- 6 Learning Transient Performance with Cutoff-Frequency Phase-In -- [DELETE] -- 6.1 Upper Bound of Trajectory Length for Good Learning Transient -- 6.2 Cutoff-Frequency Phase-In Method -- 6.3 Sliding Cutoff-Frequency Phase-In Method -- 6.4 Robot Case Study with Experimental Results -- 6.4.1 Parameter Selection -- 6.4.2 Overcoming Initial Position Offset -- 6.4.3 Improving Tracking Accuracy -- 6.5 Conclusion -- References -- 7 Pseudo-Downsampled ILC -- 7.1 Downsampled Learning -- 7.1.1 Pseudo-Downsampled ILC -- 7.1.2 Two-Mode ILC -- 7.2 Learning Data Processing -- 7.2.1 Signal Extension -- 7.2.2 Anti-aliasing Filtering and Anti-imaging Filtering -- 7.2.3 Simulation Results -- 7.3 Convergence Analysis -- 7.3.1 Convergence of Pseudo-Downsampled ILC -- 7.3.2 Convergence Analysis of Two-Mode ILC -- 7.4 Experimental Study of Downsampled ILC -- 7.4.1 Parameter Selection.
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7.4.2 Experimental Study of Two-Mode ILC -- 7.5 Conclusion -- References -- 8 Cyclic Pseudo-Downsampled ILC -- 8.1 Cyclic Pseudo-Downsampling ILC -- 8.2 Convergence and Robustness Analysis -- 8.3 Robot Application -- 8.3.1 Parameter Selection -- 8.3.2 Experiment of Cyclic Pseudo-Downsampled ILC -- 8.4 Conclusion -- References -- 9 Possible Future Research -- A A Robotic Test-Bed for Iterative Learning Control -- A.1 Hardware System -- A.1.1 Connections -- A.1.2 Non-Dynamic Parameters -- A.2 Software Platform for ILC -- A.2.1 Two-Level Software Architecture -- A.2.2 Simulink Diagrams -- A.3 Data Monitoring and Parameter Tuning via GUI -- A.4 Conclusion -- References.
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