论文标题
使用经验特征函数的快速，共旋转的随机最佳开环控制
Fast, Convexified Stochastic Optimal Open-Loop Control For Linear Systems Using Empirical Characteristic Functions
论文作者
论文摘要
我们考虑在存在未知干扰的情况下随机最佳控制的问题。我们通过经验特征函数来表征干扰，并采用偶然的限制方法。通过利用特征函数的特性和累积分布函数，我们可以通过圆锥，凸面低氧化剂重构非凸问题。这导致了非常快速的解决方案，这些解决方案可以确保维持概率约束。我们使用经验特征函数的分段线性近似来构建用于最佳开环控制的算法，并在两个示例上演示了我们的方法。
We consider the problem of stochastic optimal control in the presence of an unknown disturbance. We characterize the disturbance via empirical characteristic functions, and employ a chance constrained approach. By exploiting properties of characteristic functions and underapproximating cumulative distribution functions, we can reformulate a nonconvex problem by a conic, convex under-approximation. This results in extremely fast solutions that are assured to maintain probabilistic constraints. We construct algorithms for optimal open-loop control using piecewise linear approximations of the empirical characteristic function, and demonstrate our approach on two examples.
