论文标题
用于控制具有状态和输入约束的线性系统的在线凸优化算法
An online convex optimization algorithm for controlling linear systems with state and input constraints
论文作者
论文摘要
本文研究了受到点数限制的控制线性动力学系统的问题。我们提出了一种类似于在线梯度下降的算法，该算法可以处理时间变化和先验未知的凸成本函数，同时限制系统状态并输入到多型约束集。通过动态遗憾来衡量的对算法的性能的分析表明，如果成本函数的变化是及时的，则会实现透明性遗憾。最后，我们提出了一个简单的示例来说明实现细节以及算法的性能，并表明所提出的算法确保了限制满意度。
This paper studies the problem of controlling linear dynamical systems subject to point-wise-in-time constraints. We present an algorithm similar to online gradient descent, that can handle time-varying and a priori unknown convex cost functions while restraining the system states and inputs to polytopic constraint sets. Analysis of the algorithm's performance, measured by dynamic regret, reveals that sublinear regret is achieved if the variation of the cost functions is sublinear in time. Finally, we present a simple example to illustrate implementation details as well as the algorithm's performance and show that the proposed algorithm ensures constraint satisfaction.
