Machine learning–based robust trajectory tracking control for FSGR

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.

Keywords

• 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