Machine learning–based robust trajectory tracking control for FSGR. Issue 23 (16th December 2019)

• Record Type: Journal Article
• Authors: Jia, Lin; Wang, Yaonan; Zhang, Changfan; Zhao, Kaihui; Zhou, Langming
• Is Part Of: Journal of engineering. Volume 2019:Issue 23(2019)
• Page Start: 9220
• Page End: 9225
• Publication Date: 2019-12-16
• Subjects: neurocontrollers -- trajectory control -- robot dynamics -- variable structure systems -- feedforward -- adaptive control -- robust control -- grinding -- learning systems -- compensation; FSGR -- robust adaptive trajectory tracking algorithm -- free‐form surface -- metal surface production line -- robot dynamic approximation -- adaptive law -- neural network parameters -- sliding‐mode control -- approximation error -- adaptive machine learning -- machine‐learning feedforward compensation -- robust trajectory tracking control; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.9220
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.