Model Predictive Control for Flexible Joint Robots

TL;DR

This paper explores the integration of singular perturbation theory with Model Predictive Control to enhance the control of lightweight, flexible joint robots, focusing on stability, robustness, and input constraints.

Contribution

It introduces novel architectures combining singular perturbation and MPC, analyzing their effectiveness for controlling flexible joint robots with experimental validation.

Findings

Improved position control performance demonstrated experimentally.

Enhanced robustness against external forces.

Identified key parameters affecting control validity.

Abstract

Modern Lightweight robots are constructed to be collaborative, which often results in a low structural stiffness compared to conventional rigid robots. Therefore, the controller must be able to handle the dynamic oscillatory effect mainly due to the intrinsic joint elasticity. Singular perturbation theory makes it possible to decompose the flexible joint dynamics into fast and slow subsystems. This model separation provides additional features to incorporate future knowledge of the jointlevel dynamical behavior within the controller design using the Model Predictive Control (MPC) technique. In this study, different architectures are considered that combine the method of Singular Perturbation and MPC. For Singular Perturbation, the parameters that influence the validity of using this technique to control a flexible-joint robot are investigated. Furthermore, limits on the input constraints for the future trajectory are considered with MPC. The position control performance and robustness against external forces of each architecture are validated experimentally for a flexible joint robot.