论文标题
基于模型控制的对抗示例：灵敏度分析
Adversarial Examples for Model-Based Control: A Sensitivity Analysis
论文作者
论文摘要
我们提出了一种攻击依赖外部时间工程预测作为任务参数的控制器的方法。对手可以通过锻造时间来操纵控制器的成本，状态和行动，在这种情况下，在这种情况下会扰乱实际时间表。由于控制器经常在其成本和限制中编码安全要求或能源限制，因此我们将操纵称为对抗性攻击。我们表明，对基于模型的控制器的不同攻击可以增加控制成本，激活约束，甚至使控制优化问题不可行。我们使用线性二次调节器和凸模型预测控制器，作为对抗性攻击如何成功的示例，并证明了对抗性攻击对电网操作员电池存储控制任务的影响。结果，我们的方法将控制成本增加$ 8500 \％$，而能源限制则增加了$ 13 \％$ $ $ $。
We propose a method to attack controllers that rely on external timeseries forecasts as task parameters. An adversary can manipulate the costs, states, and actions of the controllers by forging the timeseries, in this case perturbing the real timeseries. Since the controllers often encode safety requirements or energy limits in their costs and constraints, we refer to such manipulation as an adversarial attack. We show that different attacks on model-based controllers can increase control costs, activate constraints, or even make the control optimization problem infeasible. We use the linear quadratic regulator and convex model predictive controllers as examples of how adversarial attacks succeed and demonstrate the impact of adversarial attacks on a battery storage control task for power grid operators. As a result, our method increases control cost by $8500\%$ and energy constraints by $13\%$ on real electricity demand timeseries.
