自动调参确保模型预测控制性能始终达标，高效又可靠。
Performance-driven Constrained Optimal Auto-Tuner for MPC
- 基于贝叶斯更新信念，目标导向探索参数空间
- 保证性能始终高于阈值，有限时间内收敛到最优
- 适合对安全性和稳定性要求高的控制场景
模型预测控制（MPC）参数调优的核心挑战在于确保系统性能持续高于某一阈值。为此，本文提出一种新方法COAT-MPC（约束最优自动调参器）。每轮调优中，COAT-MPC收集性能数据并更新后验信念，以目标导向的方式探索参数域，向乐观参数方向推进，显著提升样本效率。理论上证明，该方法在任意时刻满足性能约束的概率可任意接近1，并能在有限时间内收敛至最优性能。通过大量仿真及硬件平台对比实验，结果表明，相较于经典贝叶斯优化（BO）及其他前沿方法，COAT-MPC在自动驾驶竞速任务中显著降低约束违规次数与累积遗憾，表现更优。
原文摘要 · Abstract (English)
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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