Adversarial Model Predictive Control via Second-Order Cone Programming

James Guthrie; Enrique Mallada

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arXiv · 1909.05169

Adversarial Model Predictive Control via Second-Order Cone Programming

James Guthrie·Enrique Mallada

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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.

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James Guthrie, Enrique Mallada. 2019-09-11. Adversarial Model Predictive Control via Second-Order Cone Programming. https://arxiv.org/abs/1909.05169

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