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

Yaofei Han; Chao Gong; Jinqiu Gao

doi:10.1007/978-981-16-8066-3

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

Yaofei Han, Chao Gong, Jinqiu Gao

School of Automation

Research output: Book/Report › Book › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Publisher	Springer Nature
Number of pages	129
ISBN (Electronic)	9789811680663
ISBN (Print)	9789811680656
DOIs	https://doi.org/10.1007/978-981-16-8066-3
State	Published - 1 Jan 2022

Keywords

AC motors
Model predictive control
Open Access
Prediction accuracy
Robustness
Sliding mode observers

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10.1007/978-981-16-8066-3

Cite this

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AB - 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.

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KW - Open Access

KW - Prediction accuracy

KW - Robustness

KW - Sliding mode observers

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

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Keywords

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