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针对周期扰动提出一种基于磁悬浮作动器的非线性前馈自适应有源振动控制算法。算法中将磁悬浮作动器视为具有时变非线性的单输入输出系统,并使用径向基函数神经网络进行控制,分别采用聚类算法和随机梯度算法对其隐层中心点和输出层权值进行自适应调整。该算法摆脱了传统磁悬浮控制对模型的依赖,在正常工作条件下不需对作动器建模。仿真和实验结果表明:在单自由度主动隔振系统中,非线性自适应算法可以显著降低周期振动的能量,同时能对磁悬浮作动器的时变非线性进行有效的补偿。
Aiming at the periodic disturbance, a nonlinear feedforward adaptive active vibration control algorithm based on maglev actuator is proposed. In the algorithm, the maglev actuator is regarded as a single-input-output system with time-varying nonlinearity. The radial basis function neural network is used to control the maglev actuator. The clustering algorithm and stochastic gradient algorithm Value adaptive adjustment. The algorithm out of the traditional magnetic levitation control of the model dependence, under normal operating conditions do not need to model the actuator. The simulation and experimental results show that the nonlinear adaptive algorithm can significantly reduce the energy of periodic vibration in single degree of freedom active vibration isolation system, and can effectively compensate the time-varying nonlinearity of the magnetic suspension actuator.