论文标题
递归可行的随机预测控制使用插值初始状态约束 - 扩展版本
Recursively feasible stochastic predictive control using an interpolating initial state constraint -- extended version
论文作者
论文摘要
我们为线性系统提出了一个随机模型预测控制（SMPC）框架，但可能会受到无界干扰。 SMPC的最新状态与闭环的机会约束满意度接近，基于先前预测的名义状态或在某些情况下的区分下可能是测量状态的标称状态。我们通过在这两个极端的插值中对标称初始状态进行连续优化来改善这些初始化策略。由此产生的SMPC方案可以作为一个标准二次程序实施，与最先进的初始化策略相比，它更灵活。作为主要技术贡献，我们表明所提出的SMPC框架还确保了闭环对机会限制和合适的性能界限的满意度。
We present a stochastic model predictive control (SMPC) framework for linear systems subject to possibly unbounded disturbances. State of the art SMPC approaches with closed-loop chance constraint satisfaction recursively initialize the nominal state based on the previously predicted nominal state or possibly the measured state under some case distinction. We improve these initialization strategies by allowing for a continuous optimization over the nominal initial state in an interpolation of these two extremes. The resulting SMPC scheme can be implemented as one standard quadratic program and is more flexible compared to state-of-the-art initialization strategies. As the main technical contribution, we show that the proposed SMPC framework also ensures closed-loop satisfaction of chance constraints and suitable performance bounds.
