Iterative Learning Control Algorithms and Experimental Benchmarking

Cover
Title Page
Copyright
Contents
Preface
Chapter 1 Iterative Learning Control: Origins and General Overview
	1.1 The Origins of ILC
	1.2 A Synopsis of the Literature
	1.3 Linear Models and Control Structures
		1.3.1 Differential Linear Dynamics
	1.4 ILC for Time‐Varying Linear Systems
	1.5 Discrete Linear Dynamics
	1.6 ILC in a 2D Linear Systems/Repetitive Processes Setting
		1.6.1 2D Discrete Linear Systems and ILC
		1.6.2 ILC in a Repetitive Process Setting
	1.7 ILC for Nonlinear Dynamics
	1.8 Robust, Stochastic, and Adaptive ILC
	1.9 Other ILC Problem Formulations
	1.10 Concluding Remarks
Chapter 2 Iterative Learning Control: Experimental Benchmarking
	2.1 Robotic Systems
		2.1.1 Gantry Robot
		2.1.2 Anthromorphic Robot Arm
	2.2 Electro‐Mechanical Systems
		2.2.1 Nonminimum Phase System
		2.2.2 Multivariable Testbed
		2.2.3 Rack Feeder System
	2.3 Free Electron Laser Facility
	2.4 ILC in Healthcare
	2.5 Concluding Remarks
Chapter 3 An Overview of Analysis and Design for Performance
	3.1 ILC Stability and Convergence for Discrete Linear Dynamics
		3.1.1 Transient Learning
		3.1.2 Robustness
	3.2 Repetitive Process/2D Linear Systems Analysis
		3.2.1 Discrete Dynamics
		3.2.2 Repetitive Process Stability Theory
		3.2.3 Error Convergence Versus Along the Trial Performance
	3.3 Concluding Remarks
Chapter 4 Tuning and Frequency Domain Design of Simple Structure ILC Laws
	4.1 Tuning Guidelines
	4.2 Phase‐Lead and Adjoint ILC Laws for Robotic‐Assisted Stroke Rehabilitation
		4.2.1 Phase‐Lead ILC
		4.2.2 Adjoint ILC
		4.2.3 Experimental Results
	4.3 ILC for Nonminimum Phase Systems Using a Reference Shift Algorithm
		4.3.1 Filtering
		4.3.2 Numerical Simulations
		4.3.3 Experimental Results
	4.4 Concluding Remarks
Chapter 5 Optimal ILC
	5.1 NOILC
		5.1.1 Theory
		5.1.2 NOILC Computation
	5.2 Experimental NOILC Performance
		5.2.1 Test Parameters
	5.3 NOILC Applied to Free Electron Lasers
	5.4 Parameter Optimal ILC
		5.4.1 An Extension to Adaptive ILC
	5.5 Predictive NOILC
		5.5.1 Controlled System Analysis
		5.5.2 Experimental Validation
	5.6 Concluding Remarks
Chapter 6 Robust ILC
	6.1 Robust Inverse Model‐Based ILC
	6.2 Robust Gradient‐Based ILC
		6.2.1 Model Uncertainty – Case (i)
		6.2.2 Model Uncertainty – Cases (ii) and (iii)
	6.3 H∞ Robust ILC
		6.3.1 Background and Early Results
		6.3.2 H∞ Based Robust ILC Synthesis
		6.3.3 A Design Example
		6.3.4 Robust ILC Analysis Revisited
	6.4 Concluding Remarks
Chapter 7 Repetitive Process‐Based ILC Design
	7.1 Design with Experimental Validation
		7.1.1 Discrete Nominal Model Design
		7.1.2 Robust Design – Norm‐Bounded Uncertainty
		7.1.3 Robust Design – Polytopic Uncertainty and Simplified Implementation
		7.1.4 Design for Differential Dynamics
	7.2 Repetitive Process‐Based ILC Design Using Relaxed Stability Theory
	7.3 Finite Frequency Range Design and Experimental Validation
		7.3.1 Stability Analysis
	7.4 HOILC Design
	7.5 Inferential ILC Design
	7.6 Concluding Remarks
Chapter 8 Constrained ILC Design
	8.1 ILC with Saturating Inputs Design
		8.1.1 Observer‐Based State Control Law Design
		8.1.2 ILC Design with Full State Feedback
		8.1.3 Comparison with an Alternative Design
		8.1.4 Experimental Results
	8.2 Constrained ILC Design for LTV Systems
		8.2.1 Problem Specification
		8.2.2 Implementation of Constrained Algorithm 1 – a Receding Horizon Approach
		8.2.3 Constrained ILC Algorithm 3
	8.3 Experimental Validation on a High‐Speed Rack Feeder System
		8.3.1 Simulation Case Studies
		8.3.2 Other Performance Issues
		8.3.3 Experimental Results
		8.3.4 Algorithm 1: QP‐Based Constrained ILC
		8.3.5 Algorithm 2: Receding Horizon Approach‐Based Constrained ILC
	8.4 Concluding Remarks
Chapter 9 ILC for Distributed Parameter Systems
	9.1 Gust Load Management for Wind Turbines
		9.1.1 Oscillatory Flow
		9.1.2 Flow with Vortical Disturbances
		9.1.3 Blade Conditioning Measures
		9.1.4 Actuator Dynamics and Trial‐Varying ILC
		9.1.5 Proper Orthogonal Decomposition‐Based Reduced Order Model Design
	9.2 Design Based on Finite‐Dimensional Approximate Models with Experimental Validation
	9.3 Finite Element and Sequential Experimental Design‐based ILC
		9.3.1 Finite Element Discretization
		9.3.2 Application of ILC
		9.3.3 Optimal Measurement Data Selection
	9.4 Concluding Remarks
Chapter 10 Nonlinear ILC
	10.1 Feedback Linearized ILC for Center‐Articulated Industrial Vehicles
	10.2 Input–Output Linearization‐based ILC Applied to Stroke Rehabilitation
		10.2.1 System Configuration and Modeling
		10.2.2 Input–Output Linearization
		10.2.3 Experimental Results
	10.3 Gap Metric ILC with Application to Stroke Rehabilitation
	10.4 Nonlinear ILC – an Adaptive Lyapunov Approach
		10.4.1 Motivation and Background Results
	10.5 Extremum‐Seeking ILC
	10.6 Concluding Remarks
Chapter 11 Newton Method Based ILC
	11.1 Background
	11.2 Algorithm Development
		11.2.1 Computation of Newton‐Based ILC
		11.2.2 Convergence Analysis
	11.3 Monotonic Trial‐to‐Trial Error Convergence
		11.3.1 Monotonic Convergence with Parameter Optimization
		11.3.2 Parameter Optimization for Monotonic and Fast Trial‐to‐Trial Error Convergence
	11.4 Newton ILC for 3D Stroke Rehabilitation
		11.4.1 Experimental Results
	11.5 Constrained Newton ILC Design
	11.6 Concluding Remarks
Chapter 12 Stochastic ILC
	12.1 Background and Early Results
	12.2 Frequency Domain‐Based Stochastic ILC Design
	12.3 Experimental Comparison of ILC Laws
	12.4 Repetitive Process‐Based Analysis and Design
	12.5 Concluding Remarks
Chapter 13 Some Emerging Topics in Iterative Learning Control
	13.1 ILC for Spatial Path Tracking
	13.2 ILC in Agriculture and Food Production
		13.2.1 The Broiler Production Process
		13.2.2 ILC for FCR Minimization
		13.2.3 Design Validation
	13.3 ILC for Quantum Control
	13.4 ILC in the Utility Industries
		13.4.1 ILC Design
	13.5 Concluding Remarks
Appendix A
	A.1 The Entries in the Transfer‐Function Matrix (2.2)
	A.2 Entries in the Transfer‐Function Matrix (2.4)
	A.3 Matrices E1,E2,H1, and H2 for the Designs of (7.36) and (7.37)
References
Index
EULA