无需假设干扰分布，用少量计算构建置信区间，保障随机约束满足。
Conformal Prediction-Based MPC for Stochastic Linear Systems
- 基于共形预测构造误差轨迹的有限样本置信集
- 在未知干扰下实现联合时序约束的递归可行与满足
- 适合对计算效率和鲁棒性要求高的控制场景
针对干扰分布未知的线性系统，提出一种基于共形预测的随机模型预测控制框架，用于处理联合时序机会约束。不同于依赖参数化或高斯假设、需大量离线计算的现有方法，该方法利用共形预测构建系统误差轨迹的有限样本置信区域，计算开销极小。这些概率集合将联合时序机会约束转化为基于间接反馈的确定性闭环形式，确保递归可行性与约束满足性。进一步扩展至输出反馈情形，在仅有输出测量和噪声样本的前提下，仍可建立类似保证。数值实验表明该方法在有效性与性能上优于现有方法。
原文摘要 · Abstract (English)
We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike existing approaches that rely on parametric or Gaussian assumptions, or require expensive offline computation, the method uses conformal prediction to construct finite-sample confidence regions for the system's error trajectories with minimal computational effort. These probabilistic sets enable relaxation of the joint-in-time chance constraints into a deterministic closed-loop formulation based on indirect feedback, ensuring recursive feasibility and chance constraint satisfaction. Further, we extend to the output feedback setting and establish analogous guarantees from output measurements alone, given access to noise samples. Numerical examples demonstrate the effectiveness and advantages compared to existing approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。