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

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