Data-driven design of implicit force control for industrial robots

Standard control design for robot implicit force control is a typical example of model-based regulator synthesis. This paper proposes a method to improve closed-loop performance of standard model-based controllers for robot implicit force control in terms of closed-loop model matching, between desired and achieved closed-loop behaviour. To this end, a data-driven controller design method, based on the Virtual Reference Feedback Tuning (VRFT) approach, is introduced. Advantages in terms of robustness with respect to unknown environment stiffness are discussed and demonstrated. The effectiveness of the proposed control strategy is experimentally validated on an industrial robot equipped with a force sensor.