用贝叶斯方法提升神经网络MPC的不确定性建模与控制鲁棒性
Integrating Bayesian methods with neural network--based model predictive control: a review
- 融合贝叶斯方法与神经网络MPC，实现不确定性量化
- 现有研究性能提升不一致，缺乏统一评估标准
- 适合关注控制可靠性与可复现性的研究人员
本文综述贝叶斯方法在模型预测控制（MPC）中的应用，重点聚焦基于神经网络的建模、控制设计与不确定性量化。系统分析了已有研究的实现方式与实际效果。尽管贝叶斯方法被越来越多用于捕捉和传播MPC中的不确定性，但报告的性能提升与鲁棒性改进仍零散不一，且存在基线设置不统一、可靠性分析不足等问题。因此，我们主张建立标准化基准、开展消融实验并加强透明化报告，以严格评估贝叶斯技术在MPC中的有效性。
原文摘要 · Abstract (English)
In this review, we assess the use of Bayesian methods in model predictive control (MPC), focusing on neural-network-based modeling, control design, and uncertainty quantification. We systematically analyze individual studies and how they are implemented in practice. While Bayesian approaches are increasingly adopted to capture and propagate uncertainty in MPC, reported gains in performance and robustness remain fragmented, with inconsistent baselines and limited reliability analyses. We therefore argue for standardized benchmarks, ablation studies, and transparent reporting to rigorously determine the effectiveness of Bayesian techniques for MPC.
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