Adversarial Model Predictive Control via Second-Order Cone Programming
Optimization and Control
2019-09-12 v1 Systems and Control
Systems and Control
Abstract
We study the problem of designing attacks to safety-critical systems in which the adversary seeks to maximize the overall system cost within a model predictive control framework. Although in general this problem is NP-hard, we characterize a family of problems that can be solved in polynomial time via a second-order cone programming relaxation. In particular, we show that positive systems fall under this family. We provide examples demonstrating the design of optimal attacks on an autonomous vehicle and a microgrid.
Cite
@article{arxiv.1909.05169,
title = {Adversarial Model Predictive Control via Second-Order Cone Programming},
author = {James Guthrie and Enrique Mallada},
journal= {arXiv preprint arXiv:1909.05169},
year = {2019}
}
Comments
accepted to 2019 IEEE 58th Conference on Decision and Control (CDC)