Reinforcement-Learning-Based Robust Force Control for Compliant Grinding via Inverse Hysteresis Compensation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Haoqi Tang
  • Zhuoqing Liu
  • Tong Yang
  • Lei Sun
  • Yongchun Fang
  • Ning Sun

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3364-3375
Number of pages12
Journal / PublicationIEEE/ASME Transactions on Mechatronics
Volume28
Issue number6
Online published27 Apr 2023
Publication statusPublished - Dec 2023

Abstract

Traditional robotic grinding is prone to damage workpiece surface due to the high unmatched stiffness of manipulators and the difficulty in measuring actual contact force (ACF). Clearly, new robotic grinding combined with passive compliance devices (PCDs) driven by pneumatic actuators (PACs) definitely have wider applications. However, external disturbances and inherent complex hysteretic nonlinearities in PACs may severely degrade grinding precision. Therefore, it is a challenging issue for compliance systems to diminish hysteretic nonlinearities and maintain desired grinding force through robust control while reducing control efforts and improving response performance. To this end, this article introduces a PAC-driven PCD to make it easier to realize high-precision force control than the control of complex manipulators. For overcoming the difficulty in measuring ACF, we propose a novel control framework, where the hysteresis of the PAC is excluded from closed control loop, and its inverse compensator is utilized to accurately plan the control objective. Importantly, a reinforcement-learning-based robust controller is designed to realize the planned control objective. To the best of our knowledge, after elaborately developing the inverse hysteresis compensator under the proposed framework, this article, for the first time, presents an effective method to simultaneously realize disturbance suppression, control effort optimization, and error elimination for passive compliance systems. Finally, hardware experiments are carried out to verify the effectiveness and robustness of the proposed method. © 2023 IEEE.

Research Area(s)

  • Force, Hysteresis, Valves, Sun, Planning, Manipulators, Load modeling, Compliance systems, double-acting cylinder (DAC), inverse hysteresis compensator, robust force control, PREDICTIVE CONTROL, ADAPTIVE-CONTROL, POSITION CONTROL, SYSTEMS

Citation Format(s)

Reinforcement-Learning-Based Robust Force Control for Compliant Grinding via Inverse Hysteresis Compensation. / Tang, Haoqi; Liu, Zhuoqing; Yang, Tong et al.
In: IEEE/ASME Transactions on Mechatronics, Vol. 28, No. 6, 12.2023, p. 3364-3375.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review