论文标题
针对数据驱动的预测控制的在线中毒攻击
Online Poisoning Attacks Against Data-Driven Predictive Control
论文作者
论文摘要
数据驱动的预测控制（DPC）是针对动态未知系统的反馈控制方法。它根据过去的输入输出数据反复优化系统的未来轨迹。我们开发了一种计算中毒攻击的数值方法，该方法将加性扰动注入在线输出数据，以更改DPC优化的轨迹。该方法基于隐式区分DPC中轨迹优化的解决方案图。我们证明，在数值实验中，产生的攻击可能会导致输出跟踪误差一个比随机扰动高的数量级。
Data-driven predictive control (DPC) is a feedback control method for systems with unknown dynamics. It repeatedly optimizes a system's future trajectories based on past input-output data. We develop a numerical method that computes poisoning attacks that inject additive perturbations to the online output data to change the trajectories optimized by DPC. This method is based on implicitly differentiating the solution map of the trajectory optimization in DPC. We demonstrate that the resulting attacks can cause an output tracking error one order of magnitude higher than random perturbations in numerical experiments.
