论文标题
使用随机屏障函数和深度前后SDE的安全最佳控制
Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs
论文作者
论文摘要
本文介绍了一种新的公式，用于随机最佳控制和随机动态优化，以确保对状态和控制约束的安全性。所提出的方法将诸如前向后的随机微分方程，随机屏障函数，可区分的凸优化和深度学习等概念汇总在一起。使用上述概念，神经网络体系结构设计用于安全轨迹优化，在该轨迹优化中可以以端到端的方式进行学习。对三个系统进行仿真，以显示所提出的方法的功效。
This paper introduces a new formulation for stochastic optimal control and stochastic dynamic optimization that ensures safety with respect to state and control constraints. The proposed methodology brings together concepts such as Forward-Backward Stochastic Differential Equations, Stochastic Barrier Functions, Differentiable Convex Optimization and Deep Learning. Using the aforementioned concepts, a Neural Network architecture is designed for safe trajectory optimization in which learning can be performed in an end-to-end fashion. Simulations are performed on three systems to show the efficacy of the proposed methodology.
