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

Wenliang Liu, Noushin Mehdipour, Calin Belta
2020 arXiv   pre-print

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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
more » ... dict 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.
arXiv:2009.11468v1 fatcat:rdhuxj3xr5glxjvlbhckxnxvlq
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Wenliang Liu, Noushin Mehdipour, Calin Belta. "Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints." arXiv (2020)