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arXiv · 1909.00893

A Predictive Deep Learning Approach to Output Regulation: The Case of Collaborative Pursuit Evasion

Shashwat Shivam·Aris Kanellopoulos·Kyriakos G. Vamvoudakis·Yorai Wardi
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Abstract

In this paper, we consider the problem of controlling an underactuated system in unknown, and potentially adversarial environments. The emphasis will be on autonomous aerial vehicles, modelled by Dubins dynamics. The proposed control law is based on a variable integrator via online prediction for target tracking. To showcase the efficacy of our method, we analyze a pursuit evasion game between multiple autonomous agents. To obviate the need for perfect knowledge of the evader's future strategy, we use a deep neural network that is trained to approximate the behavior of the evader based on measurements gathered online during the pursuit.

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Shashwat Shivam, Aris Kanellopoulos, Kyriakos G. Vamvoudakis, Yorai Wardi. 2019-09-03. A Predictive Deep Learning Approach to Output Regulation: The Case of Collaborative Pursuit Evasion. https://arxiv.org/abs/1909.00893

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