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Dynamic Model Identification for Adaptive Polishing System

Hao An, Sining Zhang, Chaoran Cui, Cheng Qian, and Weiyang Lin*
International Journal of Control, Automation, and Systems, vol. 20, no. 9, pp.3110-3120, 2022

Abstract : In this paper, a decoupled and adaptive polishing system without force sensors is designed for the industrial robot to track the rapid change of the contact force and eliminate dynamical nonlinearities in the polishing process. An identification method is proposed to obtain the dynamic model of this system. The system dynamic model is composed of the nominal linear model and the nonlinear model. The parameters of the system linear time-invariant (LTI) model is identified by frequency domain response with the disturbance observer in this paper. Regarding the high order differential terms and uncertain errors of the nonlinear part of the dynamic model, the Long-Short Term Memory (LSTM) is introduced for identifying system nonlinear characteristics. The bounds of the learning rate are discussed and the LSTM stability analysis result shows that the proposed method holds the Lyapunov stability. Finally, the experimental results show that a more accurate dynamic model can be established by combing frequency domain response and LSTM.

Keyword : "Adaptive polishing system, LSTM stability analysis, nominal linear model, nonlinear identification. "

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