Real-Time Model Predictive Control for Industrial Manipulators with Singularity-Tolerant Hierarchical Task Control

TL;DR

This paper introduces a real-time linear MPC framework for industrial robots that handles multiple tasks, constraints, and singularities efficiently, achieving high update rates and improved task accuracy in simulations and real experiments.

Contribution

It develops a linear MPC approach using hierarchical control to enable fast, singularity-tolerant, multi-task robotic control with real-time performance.

Findings

Achieves over 1 kHz update frequency in control loop.

Reduces task tracking errors compared to operational space control.

Successfully validated in simulations and real industrial robot experiments.

Abstract

This paper proposes a real-time model predictive control (MPC) scheme to execute multiple tasks using robots over a finite-time horizon. In industrial robotic applications, we must carefully consider multiple constraints for avoiding joint position, velocity, and torque limits. In addition, singularity-free and smooth motions require executing tasks continuously and safely. Instead of formulating nonlinear MPC problems, we devise linear MPC problems using kinematic and dynamic models linearized along nominal trajectories produced by hierarchical controllers. These linear MPC problems are solvable via the use of Quadratic Programming; therefore, we significantly reduce the computation time of the proposed MPC framework so the resulting update frequency is higher than 1 kHz. Our proposed MPC framework is more efficient in reducing task tracking errors than a baseline based on operational space control (OSC). We validate our approach in numerical simulations and in real experiments using an industrial manipulator. More specifically, we deploy our method in two practical scenarios for robotic logistics: 1) controlling a robot carrying heavy payloads while accounting for torque limits, and 2) controlling the end-effector while avoiding singularities.