期刊
IEEE CONTROL SYSTEMS LETTERS
卷 6, 期 -, 页码 91-96出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCSYS.2021.3049917
关键词
Robustness; Safety; Trajectory; Lead; History; Cost function; Artificial neural networks; Optimal control; neural networks; autonomous systems
资金
- NSF [IIS-1723995, IIS-2024606]
This paper proposes a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that satisfies Signal Temporal Logic (STL) formulae.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据