Experimental Speedup and Stability Validation for Multi-Step MPC

Abstract In this paper, we propose a multi-step model predictive control (MPC) scheme without stabilizing constraints and/or costs. Within this work, a relaxed Lyapunov inequality is employed to verify asymptotic stability of the MPC closed loop. To this end, prior work is adapted to a trajectory based setting. The approach works for shorter prediction horizons in comparison to single-step MPC, but requires to stay in open loop for longer periods of time. We propose a technique to mitigate this drawback during runtime of the algorithm such that we benefit from the inherent robustness of single-step MPC. Then, we present a prime experimental validation of the proposed control scheme on a skid-steering mobile robot and show that the computational effort is significantly reduced.