Machine learning–based robust trajectory tracking control for FSGR. Issue 23 (16th December 2019) - Jia, Lin; Wang, Yaonan; Zhang, Changfan; Zhao, Kaihui; Zhou, Langming
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.