Reference Acceleration Model Predictive Control (RA-MPC) for Cable-Driven Robots

Abstract

In this paper, a computationally efficient model predictive control (MPC) is proposed for the trajectory tracking of cable-driven robots subject to state/input constraints. While MPC has been an effective tool in dealing with various constraints, the primary drawback is the high computational load caused by the non-convexity of the corresponding optimization problem. In order to avoid the non-convexity, the prediction model in the proposed reference acceleration MPC (RA-MPC) is simplified into a linear one by assuming the reference accelerations being taken in the future horizon steps. As a result, RA-MPC only optimizes for the instantaneous joint accelerations and the corresponding actuator commands for the current step, resulting in a convex quadratic program that can be efficiently solved. It is further shown that by properly selecting parameters, RA-MPC can be interpreted as ‘soft-CTC’ and ‘soft-LQR’, where the joint acceleration is allowed to deviate from the corresponding desired value, computed from a PD gain or an LQR gain. The effectiveness of the proposed RA-MPC are demonstrated in both simulation and hardware experiment using cable-driven robots.