Improved Output Tracking of a Flexible-Joint Arm using Neural Networks

摘要

This works presents a neural-adaptive control strategy for trajectory tracking for a two-link flexible joint robot, with experimental results. The method of backstepping with tuning functions (using analytic differentiation) guides the design, rather than using neural approximation of derivatives. Traditional tuning function design results in a weight update dominated by the last error in the backstepping design, not the output error. The novel method in this paper weights the errors in the tuning function so that the output error becomes significant in training. An additional modification ensures robustness to approximation errors. Experimental results show the improved performance compared to both derivative-estimation and normal tuning function methods.