MIMO ILC for Precision SEA robots using Input-weighted Complex-Kernel Regression

TL;DR

This paper presents a novel MIMO ILC approach using input-weighted complex kernel regression to significantly enhance the positioning accuracy and speed of SEA robots in uncertain environments.

Contribution

It introduces an input-weighted complex Gaussian process regression model and convergence conditions for MIMO ILC, improving precision despite modeling uncertainties.

Findings

90% improvement in positioning precision

10-fold increase in operating speed

Effective convergence under noise and model errors

Abstract

This work improves the positioning precision of lightweight robots with series elastic actuators (SEAs). Lightweight SEA robots, along with low-impedance control, can maneuver without causing damage in uncertain, confined spaces such as inside an aircraft wing during aircraft assembly. Nevertheless, substantial modeling uncertainties in SEA robots reduce the precision achieved by model-based approaches such as inversion-based feedforward. Therefore, this article improves the precision of SEA robots around specified operating points, through a multi-input multi-output (MIMO), iterative learning control (ILC) approach. The main contributions of this article are to (i) introduce an input-weighted complex kernel to estimate local MIMO models using complex Gaussian process regression (c-GPR) (ii) develop Ger\v{s}gorin-theorem-based conditions on the iteration gains for ensuring ILC convergence to precision within noise-related limits, even with errors in the estimated model; and (iii) demonstrate precision positioning with an experimental SEA robot. Comparative experimental results, with and without ILC, show around 90% improvement in the positioning precision (close to the repeatability limit of the robot) and a 10-times increase in the SEA robot's operating speed with the use of the MIMO ILC.