具有可变约束时域的随机模型预测控制的次优性
On the suboptimality of stochastic MPC with varying constraint horizon
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对不含终端项、仅在短约束时域施加机会约束的随机MPC，推导了依赖预测与约束时域的闭环成本上界，并通过线性二次问题验证了计算开销与性能的权衡。
AI中文摘要：
在模型预测控制（MPC）中，对整个预测时域施加随机状态约束可能会带来较高的计算开销。本文研究不含终端项的随机MPC，其中机会约束仅在较短的约束时域上施加。利用随机松弛动态规划，我们推导了平均期望闭环成本的显式上界，该上界同时依赖于预测时域和约束时域。对于具有仿射机会约束和有界均匀扰动的线性二次问题，我们通过坐标变换和约束收紧给出了确定性重构。仿真结果表明了计算开销与性能之间的权衡关系。
英文摘要：
Enforcing stochastic state constraints over the full prediction horizon in Model Predictive Control (MPC) can be computationally demanding. Here we study stochastic MPC without terminal ingredients in which chance constraints are enforced only over a shorter constraint horizon. Using stochastic relaxed dynamic programming, we derive an explicit upper bound on the average expected closed-loop cost that depends on both prediction and constraint horizons. For linear quadratic problems with affine chance constraints and bounded uniform disturbances, we provide a deterministic reformulation via coordinate transformation and constraint tightening. Simulations illustrate the trade-off between computational effort and performance.
