Disturbance and Friction Compensations in Hard Disk Drives Using Neural Networks

In this paper, we show that by using two adaptive neural networks (NNs), each of which is tailored for a specific task, the tracking performance of the hard-disk-drive (HDD) actuator can be significantly improved. The first NN utilizes accelerometer signal to detect external vibrations and compensates for its effect on HDD position via feedforward action. The second NN is designed to compensate for pivot friction. The appealing advantage of the NN compensators is that the design does not involve any information on the plant, sensor, disturbance dynamics, and friction model. The stability of the proposed scheme is analyzed by the Lyapunov criterion. Experimental results show that the tracking performance of the HDDs can be improved significantly with the use of the NN compensators as compared to the case without compensation.