Dynamic Model Identification for Whole-arm Compliance Control

doi:10.3901/JME.2022.03.045

Abstract

Abstract: Although the impedance control based on the dynamic model is independent of force sensors, simplifying the robot's complexity and realizing the whole-arm compliance, the real system's accurate dynamic model is hard to formulate. Supervised learning can identify the model through joint state regression, but the accuracy depends on the observed data's quantity and quality, and the identification model was challenging generalization in unobserved space. A recursive parameter identification method with prior dynamics knowledge are proposed to improve data efficiency and model's generalization. The joint's parameters are identified recursively with the iterative Newton-Euler dynamics algorithm, which reduces the number of saddle points and overcomes the dependence on the initial values. On this foundation, the whole-arm compliance controller is designed. The outer loop is impedance control with the identified model to realize the whole arm compliance without force sensors. The inner loop is sliding mode control with the identified model as the dynamic feedforward, and the radial basis function neural network is used to compensate for the system's dynamic uncertainty. The results show that the recursive identification algorithm can identify the complete dynamic model with a few observation data and realize the robot's whole-arm compliance.

Key words: whole-arm compliance, prior dynamics knowledge, recursive parameter identification, identification model

CLC Number:

TP242

LI Yang, ZHU Lishuang, LIU Jinyue, GUO Shijie. Dynamic Model Identification for Whole-arm Compliance Control[J]. Journal of Mechanical Engineering, 2022, 58(3): 45-54.

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