Performance-driven Constrained Optimal Auto-Tuner for MPC

2025-03-10

AI Summary

Model Predictive Control (MPC) suffers from a lack of performance guarantees during cost function parameter tuning. Method: This paper proposes a Bayesian optimization framework with explicit performance constraints. We introduce, for the first time, a Constrained Upper Confidence Bound (C-UCB) criterion that ensures—under Gaussian process modeling and posterior belief updates—the satisfaction of performance thresholds with high probability at any iteration. Coupled with a goal-directed optimistic exploration strategy, the framework enables performance-driven online learning. Contribution/Results: We theoretically prove finite-time convergence to the constrained optimal solution. Experiments on both autonomous racing simulation and real-world hardware platforms demonstrate that our method significantly reduces constraint violations and cumulative regret compared to classical and state-of-the-art Bayesian optimization approaches.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Ensures MPC system performance above threshold
• Proposes COAT-MPC for efficient parameter tuning
• Demonstrates COAT-MPC outperforms existing methods

Innovation

• COAT-MPC optimizes MPC parameters efficiently
• Ensures performance above threshold consistently
• Outperforms Bayesian Optimization in simulations