Predictive Learning Control for Unknown Nonaffine Nonlinear Systems: Theory and Applications

✍ Scribed by Qiongxia Yu, Ting Lei, Fengchen Tian, Zhongsheng Hou, Xuhui Bu

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 219
• Series: Intelligent Control and Learning Systems, 8
• Category: Library

✦ Synopsis

This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems and related fields.

✦ Table of Contents

Preface
Contents
1 Introduction
1.1 Predictive Control
1.2 Learning Control
1.3 Predictive Learning Control
1.4 Preview of This Monograph
References
Part I Theory
2 Predictive Iterative Learning Control for Unknown Systems
2.1 Introduction
2.2 Problem Formulation
2.3 Predictive ILC Design
2.4 Simulation Validation
2.5 Conclusion
References
3 Constrained Predictive Iterative Learning Control
3.1 Introduction
3.2 Problem Formulation
3.3 Constrained Predictive ILC Design
3.4 Simulation Validation
3.5 Conclusion
References
4 Predictive Iterative Learning Control for Systems with Varying Trial Lengths
4.1 Introduction
4.2 Problem Formulation
4.3 Data Compensation-Based Predictive ILC Design
4.4 Simulation Validation
4.5 Conclusion
References
5 Predictive Iterative Learning Control for Systems with Unknown Time Delay
5.1 Introduction
5.2 Problem Formulation
5.3 Time Delay Compensation-Based Predictive ILC Design
5.4 Simulation Validation
5.5 Conclusion
References
6 Predictive Iterative Learning Control for Systems with Full Available States
6.1 Introduction
6.2 Problem Formulation
6.3 Full-State Observer-Based Predictive ILC Design
6.3.1 Full-State Observer Design
6.3.2 Predictive Model Construction
6.3.3 Predictive ILC Design
6.4 Simulation Validation
6.5 Conclusion
References
7 Predictive Iterative Learning Control for Systems with Unavailable States
7.1 Introduction
7.2 Problem Formulation
7.3 Reduced-Order Observer-Based Predictive ILC Design
7.3.1 Reduced-Order Observer Design
7.3.2 Predictive Model Construction
7.3.3 Predictive ILC Design
7.4 Simulation Validation
7.5 Conclusion
References
Part II Applications
8 High-Speed Train Automatic Operation Systems
8.1 Introduction
8.2 Train Dynamics and Problem Formation
8.2.1 Dynamics Description of HST
8.2.2 Control Objective
8.3 RBFNN-Based PILC Design
8.4 Simulation Validation
8.5 Conclusion
References
9 Medium-Scale Two-Region Urban Road Networks
9.1 Introduction
9.2 The State of the Art for Control of Urban Road Networks
9.2.1 The Purpose of Urban Road Traffic Control
9.2.2 The History and Development of Urban Road Traffic Control
9.2.3 The Classification of Urban Road Traffic Control
9.3 One-Step Model Free Adaptive Predictive Learning Perimeter Control
9.3.1 Traffic Dynamics for Two-Region Urban Traffic Systems
9.3.2 Methodology
9.3.3 Numerical Simulation Results
9.4 Multi-step Model Free Adaptive Predictive Learning Perimeter Control
9.4.1 Methodology
9.4.2 Numerical Simulation Results
9.5 Conclusion
References
10 Large-Scale Multi-region Urban Road Networks
10.1 Introduction
10.2 One-Step Model Free Adaptive Predictive Learning Perimeter Control
10.2.1 Dynamics for the Large-Scale Multi-region Urban Road Network
10.2.2 Methodology Framework
10.2.3 Simulation Results
10.3 Multi-step Model Free Adaptive Learning Route Guidance and Perimeter Control
10.3.1 Dynamics Model of the MRUTS
10.3.2 Methodology Framework
10.3.3 Numerical Simulation Results
10.4 Conclusion
References