A gradient-based track-following controller optimization for hard disk drive

This paper presents a gradient-based parameter optimization method to find the optimal compensator that minimizes the standard deviation (sigma(PES)) of the position error signal (PES) in a hard disk drive servo system. By using the plant response data and the PES gradient information based on the nominal plant model, optimal digital controllers that minimized the 3sigma(PES) of a plant with uncertainty were selected within a pre-found robust stable region. As a result, an optimal track-following controller that minimized the standard deviation of the measured PES (sigma(PES)) was able to be obtained without the prior knowledge of the disturbance and noise model. Furthermore, we proved that if the measurement noise is white, an optimal controller that minimizes the 3sigma(PES). also minimizes the 3sigma(PES). Both simulation and implementation results suggest that such a gradient-based search process is faster than nongradient optimization methods such as Random Neighborhood Search and genetic algorithms.