Title :
NRRO Rejection using Online Iterative Control for High Density Data Storage on a PC-Based Spinstand Servo System
Author :
Pang, Chee Khiang ; Wong, Wai Ee ; Guo, Guoxiao ; Chen, Ben M. ; Lee, Tong Heng
Author_Institution :
A´´STAR Data Storage Inst., Singapore
Abstract :
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 H2 -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 3sigma NRRO and suppression of baseline NRRO spectrum.
Keywords :
Riccati equations; control engineering computing; digital control; disc drives; gradient methods; hard discs; iterative methods; learning systems; linear matrix inequalities; servomechanisms; transfer functions; H2 -norm; NRRO rejection; PC-based spinstand servo system; algebraic Riccati equations; gradient estimates; hard-disk drives; high density data storage; linear matrix inequalities; nonrepeatable run-out; online iterative control; position error signal; transfer function; Control systems; Density measurement; Error correction; Iterative algorithms; Linear matrix inequalities; Memory; Position measurement; Riccati equations; Servomechanisms; Transfer functions; HDD (Hard-Disk Drives); NRRO; PES; SSW (Self Servotrack Writing); STW (Servo Track Writing); iterative control;
Conference_Titel :
American Control Conference, 2007. ACC '07
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0988-8
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2007.4282238
