Open AccessMachine learning–based robust trajectory tracking control for FSGR
Author(s) -
Jia Lin,
Wang Yaonan,
Zhang Changfan,
Zhao Kaihui,
Zhou Langming
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9220
Subject(s) - control theory (sociology) , computer science , trajectory , feed forward , compensation (psychology) , artificial neural network , scheme (mathematics) , tracking error , robot , iterative learning control , sliding mode control , machine tool , adaptive control , tracking (education) , adaptive learning , control engineering , artificial intelligence , control (management) , engineering , nonlinear system , mathematics , mechanical engineering , psychology , mathematical analysis , pedagogy , physics , astronomy , quantum mechanics , psychoanalysis
Here, a robust adaptive trajectory tracking algorithm is proposed for free‐form surface grinding robot (FSGR) in metal surface production line. Machine‐learning method is used for robot dynamic approximation which is hard to obtain directly. Adaptive law is proposed to adjust the neural network parameters. Sliding‐mode control is employed to deal with the disturbance, joint friction, and approximation error of the adaptive machine learning. The scheme based on machine‐learning feedforward compensation can significantly reduce the chattering of sliding mode. The performance of the proposed control scheme is illustrated through simulations.