Worst-case satisfaction of STL specifications using feedforward neural network controllers: A lagrange multipliers approach

Shakiba Yaghoubi, Georgios Fainekos

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, a reinforcement learning approach for designing feedback neural network controllers for nonlinear systems is proposed. Given a Signal Temporal Logic (STL) specification which needs to be satisfied by the system over a set of initial conditions, the neural network parameters are tuned in order to maximize the satisfaction of the STL formula. The framework is based on a max-min formulation of the robustness of the STL formula. The maximization is solved through a Lagrange multipliers method, while the minimization corresponds to a falsification problem. We present our results on a vehicle and a quadrotor model and demonstrate that our approach reduces the training time more than 50 percent compared to the baseline approach.

Original languageEnglish (US)
Article numbera107
JournalACM Transactions on Embedded Computing Systems
Volume18
Issue number5s
DOIs
StatePublished - Oct 2019

Keywords

  • Neural network controller
  • Reinforcement learning
  • Signal temporal logic

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

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