確率論的モデル予測制御アプローチによる欺瞞攻撃に対するネットワーク制御システムのセキュリティ制御

This article focuses on designing an innovative security control methodology for linear networked control systems under deception attacks, which are stochastically attacked by two unknown-bounded deception signals. Firstly, an ambiguity set is introduced to characterize all potential deception signals that satisfy the identical mean-covariance constraints instead of bounded constraints. Then, chance constraints concerning system state and control variables are formulated to mitigate conservatism in the security control design. On the basis of the principle of distributionally robust optimization, chance constraints are addressed by handling a deterministic convex reformation problem. Subsequently, a stochastic model predictive control approach is deployed to realize the recursive feasibility and convergence of the controlled model. Finally, different scenarios of malicious attacks concerning the DC-DC boost converter are presented with the aim of validating the superiority of the designed approach.