Inherently Robust Suboptimal MPC for Autonomous Racing With Anytime Feasible SQP
Logan Numerow, Andrea Zanelli, Andrea Carron, Melanie N. Zeilinger
Abstract
In this paper, we propose an efficient inexact model predictive control (MPC) strategy for autonomous miniature racing with inherent robustness properties. We rely on a feasible sequential quadratic programming (SQP) algorithm capable of generating feasible intermediate iterates such that the solver can be stopped after any number of iterations, without jeopardizing recursive feasibility. Furthermore, we present the design of suitable terminal ingredients and prove how their combination with the solver ensures constraint satisfaction for any sufficiently small disturbance affecting the system's dynamics. We validate the effectiveness of the proposed strategy in simulation and by deploying it in a physical experiment with autonomous miniature race cars.