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针对离散混沌系统,提出一种基于融合Powell法的粒子群优化策略(Powell-PSO算法)的神经滑模等效控制方法.该方法通过将BP神经网络的输出作为滑模等效控制的切换部分的系数,有效地克服了传统滑模等效控制的抖振现象;利用Powell-PSO算法对神经滑模控制器的参数进行全局优化,提高了离散混沌系统的控制品质.仿真实验结果表明,所提出的方法无需了解离散混沌系统精确模型,具有响应速度快、控制精度高以及抗干扰能力强的优点.
Aiming at the discrete chaotic system, a sliding mode equivalent control method based on the Powell-PSO (PSO) method based on the Powell-PSO method is proposed. The output of the BP neural network is used as the switching part of the equivalent control of the sliding mode Which can effectively overcome the chattering phenomenon of the traditional sliding mode equivalent control. Powell-PSO algorithm is used to optimize the parameters of the neural network sliding mode controller to improve the control quality of the discrete chaotic system. Simulation results show that, The proposed method does not need to know the precise model of discrete chaotic system and has the advantages of fast response, high control precision and strong anti-interference ability.