Performance-Driven Constrained Optimal Auto-Tuner for MPC
Albert Gassol Puigjaner, Manish Kumar Prajapat, Andrea Carron, Andreas Krause, Melanie N. Zeilinger · IEEE Robotics and Automation Letters · 2025
A key challenge in tuning Model Predictive Control (MPC) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address this challenge, we propose a novel method,COAt-MPC, Constrained Optimal Auto-Tuner forMPC. With every tuning iteration,COAt-MPCgathers performance data and learns by updating its posterior belief. It explores the tuning parameters' domain towards optimistic parameters in a goal-directed fashion, which is key to its sample efficiency. We theoretically analyzeCOAt-MPC, showing that it satisfies performance constraints with arbitrarily high probability at all times and provably converges to the optimum performance within finite time. Through comprehensive simulations and comparative analyses with a hardware platform, we demonstrate the effectiveness ofCOAt-MPCin comparison to classical Bayesian Optimization (BO) and other state-of-the-art methods. When applied to autonomous racing, our approach outperforms baselines in terms of constraint violations and cumulative regret over time.