Adaptive Model Predictive Control for Differential-Algebraic Systems towards a Higher Path Accuracy for Physically Coupled Robots

TL;DR

This paper introduces an adaptive model predictive control approach for physically coupled robots, significantly improving path tracking accuracy by accounting for uncertainties and elasticity in multi-robot systems.

Contribution

It presents a novel differential-algebraic system model and an adaptive MPC that enhances path accuracy in physically coupled robots, validated through real data and simulations.

Findings

Achieved 88.6% reduction in path tracking error in simulation.

Developed a differential-algebraic model verified with real execution data.

Online estimation of uncertain kinematic parameters improves control performance.

Abstract

The physical coupling between robots has the potential to improve the capabilities of multi-robot systems in challenging manufacturing processes. However, the path tracking accuracy of physically coupled robots is not studied adequately, especially considering the uncertain kinematic parameters, the mechanical elasticity, and the built-in controllers of off-the-shelf robots. This paper addresses these issues with a novel differential-algebraic system model which is verified against measurement data from real execution. The uncertain kinematic parameters are estimated online to adapt the model. Consequently, an adaptive model predictive controller is designed as a coordinator between the robots. The controller achieves a path tracking error reduction of 88.6% compared to the state-of-the-art benchmark in the simulation.