Power Balance-Based Recursive Composite Learning Robot Control With Reduced Computational Burden

Abstract

To enhance robustness against noise resulting from velocity measurement and acceleration estimation in robot online identification and adaptive control, the robot dynamics should be filtered and parameterized to generate a filtered regression matrix regarding identifiable parameters. However, generating a filtered regression matrix is complicated for robots with high degrees of freedom (DoFs). The power balance model (PBM) of robots with spatial notations stands out as an effective option for online applications owing to its simplicity in generating an easily computed and acceleration-free filtered regression vector. This paper proposes a PBM-based recursive composite learning robot control (RCLRC) method to enhance parameter convergence so as to boost tracking control. Based on the PBM, a filtered regressor with a computational complexity of O(n) (instead of O(n<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) to O(n<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup>) for its dynamic model-based counterpart) is employed to calculate an excitation matrix, and a generalized regression equation for composite parameter update is normalized to provide more uniform convergence rates across all parameter components. Experiments on a 7-DoF robot manipulator have shown that the proposed PBM-RCLRC outperforms state-of-the-art methods on parameter estimation and tracking control.