NRRO Rejection using Online Iterative Control for High Density Data Storage on a PC-Based Spinstand Servo System

• Published in: 2007 American Control Conference pp. 1514 - 1519
• Main Authors: Chee Khiang Pang, Wai Ee Wong, Guoxiao Guo, Chen, B.M., Tong Heng Lee
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2007
• Subjects: Control systems; Density measurement; Error correction; HDD (Hard-Disk Drives); Iterative algorithms; iterative control; Linear matrix inequalities; Memory; NRRO; PES; Position measurement; Riccati equations; Servomechanisms; SSW (Self Servotrack Writing); STW (Servo Track Writing); Transfer functions
• ISBN: 9781424409884; 1424409888
• ISSN: 0743-1619; 2378-5861
• DOI: 10.1109/ACC.2007.4282238

In this paper, an OICA (Online Iterative Control Algorithm) by setting measured PES (Position Error Signal) into the servo system to achieve high track densities through minimizing the square of the H 2 -norm of the transfer function from NRRO (Non-Repeatable Run-Out) disturbance sources to true PES is proposed without having to solve any AREs (Algebraic Riccati Equations) and LMIs (Linear Matrix Inequalities). An online RRO (Repeatable Run-Out) estimator is constructed to extract NRRO components for gradient estimates, hence preventing the controller parameters from being trapped in a local minima. Experimental results on a PC-based servo system for a spinstand [12] show an improvement of 22% in 3σ NRRO and suppression of baseline NRRO spectrum.