论文标题
随机MPC具有实现自适应约束的紧缩
Stochastic MPC with Realization-Adaptive Constraint Tightening
论文作者
论文摘要
本文在存在加性干扰的情况下为线性时间流体系统提供了随机模型预测控制器（SMPC）。干扰的分布未知，被认为具有有界的支持。基于样本的策略用于计算鲁棒性限制所必需的干扰序列集。这些集合是使用从其支持中提取的干扰样本离线构造的。对于在线MPC实施，我们提出了一种新颖的机会约束重新制定策略，在该策略中，通过基于沿轨迹的先前实现的干扰来调整离线计算的集合来计算约束拧紧。提出的MPC是可行的，可以以更高的离线计算时间为代价来降低现有SMPC方法的保守主义。数值模拟证明了所提出的方法的有效性。
This paper presents a stochastic model predictive controller (SMPC) for linear time-invariant systems in the presence of additive disturbances. The distribution of the disturbance is unknown and is assumed to have a bounded support. A sample-based strategy is used to compute sets of disturbance sequences necessary for robustifying the state chance constraints. These sets are constructed offline using samples of the disturbance extracted from its support. For online MPC implementation, we propose a novel reformulation strategy of the chance constraints, where the constraint tightening is computed by adjusting the offline computed sets based on the previously realized disturbances along the trajectory. The proposed MPC is recursive feasible and can lower conservatism over existing SMPC approaches at the cost of higher offline computational time. Numerical simulations demonstrate the effectiveness of the proposed approach.
