Resilient Adaptive Secure Control for MIMO Switched CPSs Under Unknown Deception Attacks

This paper investigates the control problem of multi-input multi-output (MIMO) switched cyber-physical systems (CPSs) subject to unknown sensor and actuator deception attacks. First, due to the presence of unknown deception attacks, the system states become compromised, preventing sensors from obtaining accurate state variables for feedback. To solve this problem, a novel coordinate transformation and attack compensator are designed using the compromised variables. Furthermore, radial basis function neural networks (RBF NNs) and dynamic surface control (DSC) techniques are integrated to mitigate actuator deception attacks. Considering limited communication resources, a two-bit-triggered (TBT) mechanism is proposed to further save communication resources. Under an arbitrary switching rule and the proposed control scheme, it is guaranteed that all signals in the closed-loop system are uniformly ultimately bounded, and the stability error converges to a small neighborhood of the origin. Finally, the effectiveness of the proposed scheme is demonstrated through a simulation example.