Neural network-based practical prescribed time adaptive tracking control for nonlinear networked control systems under deception attacks.

Abstract

This paper investigates the neural network-based practical prescribed time adaptive tracking control problem for strict feedback nonlinear networked control systems with deception attacks in both sensors and actuators. To reduce the detrimental effects of deception attacks, an attack compensator is constructed based on compromised states and neural network technique. Then, a practical prescribed time function is introduced such that the tracking error does not violate the constraint boundary within the prescribed time, which ensures the transient and steady-state performances of the closed-loop system. Besides, the first-order sliding mode differentiator is designed to estimate the derivation of the virtual control laws, which eliminates the "complexity explosion". Mathematically, it is demonstrated that all the signals in the closed-loop system are bounded, and the tracking error converges to a predetermined boundary within a prescribed time. Eventually, a numerical example and an application example of the single-link robotic arm system are adopted to exhibit the effectiveness of the acquired control algorithm.