论文标题
安全随机模型预测控制
Safe Stochastic Model Predictive Control
论文作者
论文摘要
将高效和安全控制的安全 - 关键系统结合起来是具有挑战性的。强大的方法可能过于保守，而概率控制器则需要在效率和安全性之间进行权衡。在这项工作中，我们提出了一种安全算法，该算法与具有添加性不确定性和多重约束的线性系统的任何随机模型预测控制方法兼容。该安全算法允许使用随机模型预测控制的乐观控制输入，只要安全备份计划者可以确保在满足符合有限不确定性的硬性约束方面的安全性。除了确保安全行为外，提出的随机模型预测控制算法还确保了系统来源的递归可行性和输入到州的稳定性。在数值模拟中证明了安全随机模型预测控制算法的好处，与纯粹或随机的预测控制器相比，强调了优势。
Combining efficient and safe control for safety-critical systems is challenging. Robust methods may be overly conservative, whereas probabilistic controllers require a trade-off between efficiency and safety. In this work, we propose a safety algorithm that is compatible with any stochastic Model Predictive Control method for linear systems with additive uncertainty and polytopic constraints. This safety algorithm allows to use the optimistic control inputs of stochastic Model Predictive Control as long as a safe backup planner can ensure safety with respect to satisfying hard constraints subject to bounded uncertainty. Besides ensuring safe behavior, the proposed stochastic Model Predictive Control algorithm guarantees recursive feasibility and input-to-state stability of the system origin. The benefits of the safe stochastic Model Predictive Control algorithm are demonstrated in a numerical simulation, highlighting the advantages compared to purely robust or stochastic predictive controllers.
