LQR Based Training of Adaptive Neuro-Fuzzy Controller

被引：5

作者：

Rashid, Usman ^{[1
]}

Jamil, Mohsin ^{[1
]}

Gilani, Syed Omer ^{[1
]}

Niazi, Imran Khan ^{[2
,3
]}

机构：

[1] Natl Univ Sci & Technol, Dept Robot & Artificial Intelligence, Islamabad, Pakistan

[2] Aalborg Univ, Dept Hlth Sci & Technol, Aalborg, Denmark

[3] New Zealand Coll Chiropract, Ctr Chiropract Res, Auckland, New Zealand

来源：

ADVANCES IN NEURAL NETWORKS: COMPUTATIONAL INTELLIGENCE FOR ICT | 2016年 / 54卷

关键词：

Active suspension system; Quarter vehicle model; LQR; Fuzzy inference system; Adaptive neuro fuzzy inference system; Hardware-in-loop; SYSTEM; ANFIS;

D O I：

10.1007/978-3-319-33747-0_31

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The focus of this paper is the design and implementation of adaptive network based fuzzy inference (ANFIS) controller by using the training data obtained from a system controlled by Linear Quadratic Regulator (LQR). This work is motivated by the need to remove stochastic observer required for LQR in noisy environments while at the same time to have optimal performance. This theory is validated by taking the well investigated case of Active Suspension System of a Quarter Vehicle. The performance of the obtained ANFIS controller is tested using Quanser (c) Active Suspension Plant. It is observed that the ANFIS controller gives good close loop performance, while removing the requirement of a stochastic observer.

引用

页码：311 / 322

页数：12

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