Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints

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 determin...

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Bibliographic Details
Published inIEEE control systems letters Vol. 6; pp. 91 - 96
Main Authors Liu, Wenliang, Mehdipour, Noushin, Belta, Calin
Format Journal Article
LanguageEnglish
Published IEEE 2022
Subjects
Artificial neural networks
autonomous systems
Cost function
History
Lead
neural networks
Optimal control
Robustness
Safety
Trajectory
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Summary: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.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2021.3049917