Adaptive inverse control for settling performance improvements

Single-track hard disk drive (HDD) seek performance is measured by settling time, t_s, defined as the time from the arrival of a seek command until the measured position reaches and stays within an acceptable distance from the target track. Previous work has shown feedforward dynamic inversion, coupled with an aggressive desired trajectory y_d, is capable of achieving high performance settling times when the closed-loop dynamics are time-invariant and accurately modeled. In contrast, we describe an adaptive inversion procedure in this paper which removes the requirement for accurate initial models and tracks the position-variant dynamics present in our Servo Track Writer (STW) experimental apparatus. The proposed indirect adaptive inversion algorithm relies on a recursive least squares (RLS) estimate of the closed-loop dynamics. Pre-filtering of the RLS input signals and covariance resetting are necessary additions to the baseline adaptive algorithm in order to achieve fast settling times. Compared to the nonadaptive solution with accurate system identification, we show the adaptive algorithm achieves a 22% reduction in average settling time and a 53% reduction in settling time standard deviation.