From Instantaneous to Predictive Control: A More Intuitive and Tunable MPC Formulation for Robot Manipulators

TL;DR

This paper introduces a practical MPC formulation for robot manipulators that combines intuitive tuning with enhanced predictive performance, validated through a surface-following task.

Contribution

A new MPC formulation that simplifies tuning by retaining interpretable parameters while improving performance with a prediction horizon.

Findings

Improved control performance over traditional instantaneous control.

Eases the tuning process for MPC in robotic applications.

Validated on a surface-following task with promising results.

Abstract

Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To address this hurdle, we propose a practical MPC formulation which retains the more interpretable tuning parameters of the instantaneous control approach while enhancing the performance through a prediction horizon. The formulation is motivated at hand of a simple example, highlighting the practical tuning challenges associated with typical MPC approaches and showing how the proposed formulation alleviates these challenges. Furthermore, the formulation is validated on a surface-following task, illustrating its applicability to industrially relevant scenarios. Although the research is presented in the context of robot manipulator control, we anticipate that the formulation is more broadly applicable.