Study on tracking control of micro hard disk dual-stage servo systems based on neural network

Based on neural network, this work is to investigate one control technique of micro hard disk dual-stage servo systems, due to the difficulty that single-stage control techniques suppress noisy disturbance hardly. Firstly, the models of the voice coil motor and micro-actuator are identified by means of BP network and Elman network, respectively; secondly, the controllers of these two models are designed in terms of PID network, in which initial weights and learning rates are gained by optimal preservation genetic algorithm(OPGA). Thirdly, the proposed controllers are applied to micro disk dual-stage servo control systems. Experimental results illustrate that the slider head can effectively track given reference signal while its output error can obtain compensation through the output of the micro-actuator.