Discrete-Time Adaptive Iterative Learning Control: From Model-Based to Data-Driven (Intelligent Control and Learning Systems, 1)

✍ Scribed by Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 211
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

✦ Table of Contents

Preface
Contents
1 Introduction
1.1 Brief Review of Iterative Learning Control
1.2 Adaptive Iterative Learning Control
1.3 Discrete-Time Adaptive Iterative Learning Control
1.4 Structure of This Monograph
References
Part I Model-Based Discrete-Time Adaptive ILC
2 Discrete-Time Adaptive ILC for Nonlinear Parametric Systems
2.1 Introduction
2.2 Problem Formulation
2.3 Controller Design
2.4 Convergence Analysis
2.5 Illustrative Simulations
2.6 Extension to Nonlinear Systems with Unknown Time-Varying Input Gain
2.6.1 Problem Formulation
2.6.2 Controller Design
2.6.3 Convergence Analysis
2.6.4 Illustrative Simulations
2.7 Discrete-Time Adaptive ILC for Higher Order Parametric Systems
2.7.1 Problem Formulation
2.7.2 Controller Design
2.7.3 Convergence Analysis
2.7.4 Illustrative Simulations
2.8 Summary
References
3 Data-Weighted Discrete-Time Adaptive ILC
3.1 Introduction
3.2 Problem Formulation
3.3 Controller Design
3.4 Convergence Analysis
3.5 Extension to MIMO Systems with Multiple Parameters and Time-Varying Input Gains
3.5.1 Problem Formulation
3.5.2 Controller Design
3.5.3 Convergence Analysis
3.6 Illustrative Simulations
3.7 Summary
References
4 Nonlinearity Estimator-Based Discrete-Time Adaptive ILC
4.1 Introduction
4.2 Problem Formulation
4.3 Controller Design
4.4 Convergence Analysis
4.5 Illustrative Simulations
4.6 Summary
References
5 Neural Network-Based Discrete-Time Adaptive ILC
5.1 Introduction
5.2 Problem Formulation
5.3 Controller Design
5.4 Convergence Analysis
5.5 Illustrative Simulations
5.6 Summary
References
6 Distributed Discrete-Time Adaptive ILC for Multi-Agent Systems
6.1 Introduction
6.2 Problem Formulation
6.2.1 Preliminaries
6.2.2 Problem Formulation
6.3 Controller Design
6.4 Convergence Analysis
6.5 Extended Distributed DAILC to MASs with Multiple Parameters …
6.5.1 Problem Formulation
6.5.2 Controller Design
6.5.3 Convergence Analysis
6.6 Illustrative Simulations
6.7 Summary
References
Part II Data-Driven Discrete-Time Adaptive ILC
7 Data-Driven DAILC for Nonlinear Nonaffine Systems
7.1 Introduction
7.2 Data-Driven DAILC for SISO Nonaffined Nonlinear Systems
7.2.1 Problem Formulation
7.2.2 Controller Design
7.2.3 Convergence Analysis
7.2.4 Illustrative Simulations
7.3 Data-Driven DAILC for MIMO Nonaffined Nonlinear Systems
7.3.1 Problem Formulation
7.3.2 Controller Design
7.3.3 Convergence Analysis
7.3.4 Illustrative Simulations
7.4 Summary
References
8 Multi-Input Enhanced Data-Driven Discrete-Time Adaptive ILC
8.1 Introduction
8.2 Problem Formulation
8.3 Controller Design
8.4 Convergence Analysis
8.5 Illustrative Simulations
8.6 Summary
References
9 Data-Driven Discrete-Time Adaptive ILC for Terminal Tracking
9.1 Introduction
9.2 Data-Driven Terminal DAILC for Iteration-Varying Target Points
9.2.1 Problem Formulation
9.2.2 Controller Design
9.2.3 Convergence Analysis
9.2.4 Illustrative Simulations
9.3 Data-Driven Terminal DAILC with Nonidentical Conditions of Initial States and Target Points
9.3.1 Problem Formulation
9.3.2 Controller Design
9.3.3 Convergence Analysis
9.3.4 Illustrative Simulations
9.4 Data-Driven Terminal DAILC Based on Stochastic High Order Internal Model
9.4.1 Problem Formulation
9.4.2 Controller Design
9.4.3 Convergence Analysis
9.4.4 Illustrative Simulations
9.5 Summary
References