Finite-Time Adaptive Tracking Control for Non-Linear Networked Control Systems With Prescribed Performance Under Unknown Deception Attacks

This paper investigates the finite-time adaptive tracking control problem for non-linear networked control systems with prescribed performance under unknown deception attacks. To mitigate the effects caused by unknown deception attacks, a series of auxiliary signals and attack compensators are reasonably constructed to overcome the unavailability problem of the compromised state variables. Besides, the neural network approximation technique is utilized to address unknown non-linear terms and actuator deception attacks. Further, an equivalent system model is acquired by introducing the intermediate transformations of the tracking error and the prescribed performance function. Then, a finite-time adaptive tracking controller is designed based on the neural network and the backstepping techniques. Moreover, it is mathematically rigorously proved that all the signals of the closed-loop system are bounded and the tracking error converges within the predefined boundary in a finite-time. Finally, an example application of a single-link robotic arm system is applied to verify the effectiveness of the designed control algorithm.