论文标题
信号时间逻辑规格的经常性神经网络控制器受安全限制的约束
Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints
论文作者
论文摘要
我们提出了一个基于经常性神经网络（RNN）的框架，以确定离散时间系统的最佳控制策略，该系统需要满足作为信号时间逻辑（STL）公式的规格。 RNN可以随着时间的推移存储系统的信息，因此，使我们能够确定对STL公式中指定的动态时间要求的满意度。给定STL公式，一个令人满意的系统执行和相应的控制策略的数据集，我们可以根据系统的当前和以前的状态在每个时间使用RNN来预测控制策略。我们使用控制屏障功能（CBF）来确保预测的控制政策的安全。我们验证我们的理论表述，并通过模拟受到部分未知的安全限制，在最佳控制问题中证明了其性能。
We propose a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that is required to satisfy specifications given as Signal Temporal Logic (STL) formulae. RNNs can store information of a system over time, thus, enable us to determine satisfaction of the dynamic temporal requirements specified in STL formulae. Given a STL formula, a dataset of satisfying system executions and corresponding control policies, we can use RNNs to predict a control policy at each time based on the current and previous states of system. We use Control Barrier Functions (CBFs) to guarantee the safety of the predicted control policy. We validate our theoretical formulation and demonstrate its performance in an optimal control problem subject to partially unknown safety constraints through simulations.
