Model Predictive Control for AC Motors: Robustness and Accuracy Improvement Techniques

✍ Scribed by Yaofei Han, Chao Gong, Jinqiu Gao

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 137
• Category: Library

✦ Synopsis

This book introduces how to improve the accuracy and robustness of model predictive control. Firstly, the disturbance observation- and compensation-based method is developed. Secondly, direct parameter identification methods are developed. Thirdly, the seldom-focused-on issues such as sampling and delay problems are solved in this book. Overall, this book solves the problems in a systematic and innovative way.

Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com

✦ Table of Contents

Preface by Yaofei Han
Preface by Chao Gong
Contents
About the Editors
1 Model Predictive Control for AC Motors
1.1 Basic Knowledge of MPC
1.1.1 History of MPC
1.1.2 Implementations of MPC
1.1.3 Understanding MPC in View of Control
1.1.4 Applications of MPC
1.2 MPC for AC Motors
1.2.1 Introduction of AC IMs and SMs
1.2.2 MPC Methods for AC Motors
1.2.3 Common Problems
1.3 Implementations in MATLAB of Typical MPC
1.4 Summary
References
2 Observer-Based Robustness Improvement for FCS-MPCC Used in IMs
2.1 Problem Descriptions
2.2 Implementation of FCS-MPCC and Impacts of Parameter Mismatch on Control Performance
2.2.1 State-Space Model of IM
2.2.2 Impacts of Parameter Mismatch on Performance
2.3 Proposed Sliding Mode Disturbance Observer
2.3.1 Sliding Mode Disturbance Observer
2.3.2 Stability Analysis
2.4 Verifications
2.4.1 Case 1
2.4.2 Case 2
2.5 Summary
References
3 Parameter-Identification-Based Robustness Improvement for FCS-MPC Used in WFSMs
3.1 Problem Descriptions
3.2 Modeling of WFSMs
3.3 SM Observer-Based Parameter Identification
3.3.1 Design of Sm Observers
3.3.2 Stability Analysis
3.3.3 Observer Robustness Against Parameter Uncertainties
3.4 Implementations of SM-Observer-Based FCS-MPC
3.5 Verifications
3.5.1 Parameter Identification Results
3.5.2 FCS-MPCC Control Results
3.6 Summary
References
4 MPC Accuracy Improvement for PMSMs—Part I
4.1 Numerical Solution-Based FCS-MPCC
4.1.1 Problem Descriptions
4.1.2 Numerical Solution-Based Predicting Plant
4.1.3 Novel Calculation Delay Compensation
4.1.4 Verifications
4.2 Multi-objective FCS-MPC with Delay Compensation
4.2.1 Problem Descriptions
4.2.2 Improved Model for Multi-objective FCS-MPC
4.2.3 Implementation of Multi-objective FCS-MPC
4.2.4 Verifications
4.3 Summary
References
5 MPC Accuracy Improvement for PMSMs—Part II
5.1 Flux-Observer-Based Sub-Step FCS-MPCC
5.1.1 Problem Description
5.1.2 Impacts of LCF and FLM
5.1.3 Flux-Observer-Based Sub-Step FCS-MPCC Strategy
5.1.4 Verifications
5.2 Linearized CCS-MPC for IPMSM Flux-Weakening Control
5.2.1 Problem Description
5.2.2 Classic MPC-Based Flux-Weakening Algorithm
5.2.3 New MPC-Based Flux-Weakening Algorithm
5.2.4 Verifications
5.3 Summary
References
6 Conclusion