欺骗攻击下弹性自触发模型预测控制

Resilient self-triggered model predictive control under deception attacks

针对在欺骗攻击下自触发模型预测控制系统的安全控制问题,本文提出一种基于关键数据保护的弹性自触发模型预测控制(MPC)策略.对比现有的自触发MPC,该方法仅需对少量关键控制样本进行保护,则可保证闭环系统稳定运行,从而有效节省系统资源.首先,基于自触发MPC和欺骗攻击的特征推导标称系统与被攻击系统状态之间的误差上界,从而定量分析出欺骗攻击对系统的损害.然后,通过所获得误差上界和李雅普诺夫定理建立关键数据的选取条件并对其实施保护.最后,严格证明了在仅对关键控制样本实施保护后,被控系统仍可在欺骗攻击下保持稳定.此外,基于移动机器人和弹簧小车系统对所提算法进行了仿真实验,结果表明所提算法能够显著节省保护资源,验证了算法的有效性.

Aiming at solving the cyber security problem of self-triggered model predictive control(MPC)in cyberphysical systems under deception attacks,a resilient self-triggered MPC strategy based on the key data protection is proposed.Compared with the existing MPC methods,the proposed method only protects a small number of key control samples to ensure the stability of the system,thus effectively saving system resources.Firstly,the upper limit of the difference between the nominal and attacked states is analyzed based on the characteristics of self-triggered MPC and deception attacks,so as to quantitatively calculate the damages to the system caused by deception attacks.Then,the selection conditions of the key data are given based on the obtained upper limit of the state difference and Lyapunov stability theory.Finally,it is demonstrated that the controlled system can be operated stably under deception attacks when only key control samples are protected.Furthermore,the proposed algorithm is simulated based on the mobile robot and cart-damper-spring system,and the results show that the proposed algorithm can significantly save the protection resources,which verifies the effectiveness of the method.