基于自适应神经网络的分数阶混沌系统滑模同步

针对一类异结构不确定分数阶混沌系统的同步问题,基于Lyapunov稳定性理论和分数阶系统稳定性理论,提出一种神经网络结合干扰观测器的主动反馈控制方法. 设计一种非线性干扰观测器对干扰进行观测,通过滑模控制对未观测出的部分干扰进行补偿,最终实现分数阶混沌系统的同步.与现有方法相比,采用的模型更符合工程应用实际,且不需要已知不确定项上界.数值仿真验证了所提出方法的有效性和正确性.

Sliding mode synchronization of fractional-order chaotic systems based on adaptive neural network

Based on the Lyapunov stability theory and the fractional-order system stability theory, an active feedback control method using neural networks and disturbance observer is proposed for the robust synchronization of a class of different fractional-order chaotic systems with uncertainties. The disturbance observer is designed to observe the disturbance, and the disturbance that is not observed is compensated by sliding mode control. Finally, the synchronization of fractional-order chaotic systems is realized. Compared with the existed methods, the adopted models in the proposed approach are more in line with engineering practice, and the upper bounds of the uncertainties are not required. Numerical simulations verify the effectiveness of the proposed method.