Performance-driven Constrained Optimal Auto-Tuner for MPC

• URL: http://arxiv.org/abs/2503.07127v1
• Date: Mon, 10 Mar 2025 09:56:08 GMT
• Title: Performance-driven Constrained Optimal Auto-Tuner for MPC
• Authors: Albert Gassol Puigjaner, Manish Prajapat, Andrea Carron, Andreas Krause, Melanie N. Zeilinger,
• Abstract summary: We propose COAT-MPC, Constrained Optimal Auto-Tuner for MPC.<n>COAT-MPC gathers performance data and learns by updating its posterior belief.<n>We theoretically analyze COAT-MPC, showing that it satisfies performance constraints with arbitrarily high probability.
• Score: 36.143463447995536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 for MPC. With every tuning iteration, COAT-MPC gathers 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 analyze COAT-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 of COAT-MPC in 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.

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