Research on adaptive active vibration control using maglev actuator

A feedforward adaptive active vibration control (AVC) algorithm using maglev actuator is proposed to attenuate periodic disturbance in this paper.Maglev actuator is viewed as a single-input-single-output (SISO) system with time-varying nonlinearity in the proposed algorithm.A radial basis function (RBF) neural network is used as the controller,whose hidden-layer centers and output-layer weights are adapted according to clustering algorithms and stochastic gradient algorithms respectively.In the proposed algorithm,there is no need to model the maglev actuator under normal circumstance,which is critical and difficult in conventional maglev control.The results of simulations and experiments based on a single degree-of-freedom vibration isolation system show that the adaptive algorithm can greatly attenuate the periodic disturbance,and can efficiently compensate for the actuator's time-varying nonlinearity at the same time.