Fuzzy-based adaptive event-triggered control for nonlinear cyber-physical systems against deception attacks via a single parameter learning method

This paper investigates the adaptive fuzzy control issue of cyber-physical systems (CPSs) against unknown deception attacks and external disturbance. Based on the backstepping design framework, the uncertainty term composed of the approximation error term generated by the fuzzy logic system, the external disturbance and the deception attack signals are regarded as a whole in the recursive design process. Then, by combining the single parameter learning method with the adaptive fuzzy technique, the uncertain terms containing approximation error, disturbance and injection attacks can be transformed into the form of linear parameterization with only one unknown scalar parameter, which greatly reduce the calculation process. In addition, the dynamic event-triggered adaptive control technology can be integrated into the control design to reduce the amount of data transmission. Theoretical analysis shows that all the signals in the closed-loop system are bounded under the proposed control scheme, and the Zeno behavior is excluded. Finally, the two-stage chemical reactor and mass-spring-damper systems are employed to demonstrate the validity of the proposed adaptive fuzzy control solution.