Data-Driven Disturbance Decoupling Fault Tolerant Control for System with Deterministic Disturbance

被引：0

作者：

Gao, Tianyi ^{[1
]}

Yin, Shen ^{[1
]}

Li, Kuan ^{[1
]}

Wu, Xinwei ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China

来源：

45TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

data-driven; disturbance decoupling; subspace identification; dynamic linearization; predivtive control;

D O I：

10.1109/iecon.2019.8926990

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A data-driven disturbance decoupling FTC for system with deterministic disturbance is proposed in this paper. The algorithm of subspace identification and modified Partial least square is aided to improve the limit of dynamic linearization based predictive control. Compared with the existing dynamic linearization based predictive control, the proposed control strategy enhances its control performance by reducing its sensitivity to noise and decoupling the disturbance. The efficiency of the proposed FTC approach compared to the ynamic linearization based predictive controller is suggested in the simulation of a DC motor.

引用

页码：3749 / 3754

页数：6

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