Bioinspired composite learning control under discontinuous friction for industrial robots

TL;DR

This paper introduces a bioinspired adaptive learning control method for industrial robots with discontinuous friction, improving tracking performance without high-gain feedback by using composite error learning and data memory.

Contribution

It proposes a novel adaptive learning control approach inspired by human motor learning, utilizing composite error and data memory to enhance parameter estimation in robotic systems with discontinuous friction.

Findings

Superior transient and steady-state tracking achieved

Validated through experiments on a DENSO industrial robot

Outperforms classical feedback error learning control

Abstract

Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot.