Interaction Force Constraints for Position-Controlled Manipulator Using Linear MPC

The control of the interaction force between the environment and the manipulator in safety-critical scenarios, like surgery, search-and-rescue or industrial plant maintenance, can prevent damage or unwanted behaviour. If the manipulator software provides only position-based or velocity-based control loops, controlling the interaction forces is a challenging problem. For this reason, Model Predictive Control (MPC) has started to be adopted also in the field of real-time force control thanks to its capability of integrating constraints. In this paper, we propose a linear model predictive force control able to guarantee safe interaction with an unknown environment by constraining the interaction force. The environment is modelled together with the robot dynamics and, thanks to the estimation of the low-level controller, the proposed methodology can be applied to any robotic manipulator without a direct joint torque control loop. The MPC controller with force constraint has been validated in a real scenario using a UR5e collaborative robot in a polishing-like task.