基于CMAC网络的主动隔振控制技术

针对主动隔振系统存在非线性,提出一种基于CMAC神经网络控制器的主动控制方法。由于系统的次通道使得控制器参数无法直接利用振动误差信号来调整,提出一种改进的同时扰动自适应算法,并证明该算法在数学期望上是一种随机梯度法。针对系统的非线性情况进行了仿真和试验研究,结果表明:与传统的FXLMS自适应前馈控制方法相比,所提出的方法对于存在非线性的周期性振动的隔振效果明显。

An Active Vibration Isolation Control Method Based on CMAC Networks

An active control method based on cerebellar model articulation controller(CMAC) neural networks as controllers was presented to address the nonlinearity of an active vibration isolation system.The controllers′ parameters cannot be adjusted directly by output error signals because of a secondary path.An improved simultaneous perturbation adaptive algorithm,which proves to be a stochastic gradient approximation method in terms of mathematical expectation,was presented.Simulations and experiments were carried out to explain the nonlinearity,the results of which show that the CMAC neural network control method has better effects on the isolation of periodical vibration with nonlinearity compared with a traditional FXLMS adaptive feedforward control method.