Q-learning-based feedback linearization method for unknown dynamics

被引：0
|
作者：
Sun, Yipu [1 ,2 ,3 ,4 ]
Chen, Xin [1 ,2 ,3 ]
He, Wenpeng [1 ,2 ,3 ,4 ]
Wang, Luo [1 ,2 ,3 ,4 ]
Fukushima, Edwardo F. [4 ]
She, Jinhua [4 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Peoples R China
[3] Minist Educ, Engn Res Ctr Intelligent Technol Geoexplorat, Wuhan 430074, Peoples R China
[4] Tokyo Univ Technol, Sch Engn, Tokyo 14041, Japan
来源：
2023 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS, ICM | 2023年
基金：
中国国家自然科学基金;
关键词：
Unknown nonlinear system; input-output feedback linearization; equivalent input interference; Q-learning; NONLINEAR-SYSTEMS;
D O I：
10.1109/ICM54990.2023.10101884
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper present a feedback linearization technique for affine nonlinear systems that is independent of system dynamics. First, a input-output feedback linearization correction framework is described, and a interference estimator is employed to guarantee the stability of plant during the learning process. Then, a model-free Q-learning algorithm is presented to solve the feedback linearized controller. Finally, the position control of a single-link flexible joint manipulator system is used as an example to demonstrate the effectiveness of the method.
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收藏
页数：6
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