论文标题
强劲的输出反馈MPC，在椭圆形的不确定性下保守性减少
Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty
论文作者
论文摘要
在不确定性下对自主系统的强大设计是一个重要但具有挑战性的问题。这项工作提出了一个由州估计器和基于管子的预测控制法组成的强大控制器。考虑了椭圆形不确定性下的线性系统类别。与基于多重重点集的现有方法相反，约束收紧是从没有过度交易的椭圆形干扰集中计算出来的，从而导致较不保守的界限。提出了确保强大约束满意度和稳健稳定性的条件。此外，通过避免在集合计算中使用Minkowski总和，所提出的方法还可以扩展到高维系统。结果通过示例说明。
Robust design of autonomous systems under uncertainty is an important yet challenging problem. This work proposes a robust controller that consists of a state estimator and a tube based predictive control law. The class of linear systems under ellipsoidal uncertainty is considered. In contrast to existing approaches based on polytopic sets, the constraint tightening is directly computed from the ellipsoidal sets of disturbances without over-approximation, thus leading to less conservative bounds. Conditions to guarantee robust constraint satisfaction and robust stability are presented. Further, by avoiding the usage of Minkowski sum in set computation, the proposed approach can also scale up to high-dimensional systems. The results are illustrated by examples.
