Prescribed performance sliding mode synchronization of chaotic systems via neural network-based disturbance observer

In this paper, a novel prescribed performance sliding mode control strategy integrated with a neural network-based disturbance observer (NDO) is proposed to achieve fast and robust synchronization of chaotic systems. First, the synchronization error is transformed from the error domain (e-domain) to the performance domain (ζ-domain) by employing a prescribed performance function. Subsequently, based on the transformed error dynamics, a sliding mode surface that incorporates a delayed feedback term is designed. A synchronization controller is then developed based on the proposed sliding mode surface. To enhance system robustness against unknown disturbances, a neural network-based disturbance observer is introduced to estimate and compensate for uncertainties in real time. The stability of the closed-loop synchronization error system is rigorously proved based on the Lyapunov-Krasovskii (L-K) functional method. Finally, numerical simulation results are presented to demonstrate the effectiveness and superior performance of the proposed control strategy.