NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks
Abstract
In this paper, we propose a framework for performing state space exploration of closed loop control systems. For closed loop control systems, we introduce the notion of inverse sensitivity function and present a mechanism for approximating inverse sensitivity by a neural network. This neural network can be used for generating trajectories that reach a destination (or a neighborhood around it). We demonstrate the effectiveness of our approach by applying it to standard nonlinear dynamical systems, nonlinear hybrid systems, and also neural network based feedback control systems.
Cite
Text
Goyal and Duggirala. "NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Goyal and Duggirala. "NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/goyal2020l4dc-neuralexplorer/)BibTeX
@inproceedings{goyal2020l4dc-neuralexplorer,
title = {{NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks}},
author = {Goyal, Manish and Duggirala, Parasara Sridhar},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {697-697},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/goyal2020l4dc-neuralexplorer/}
}