A Neural-Networks-Based Adaptive Disturbance Rejection Method and Its Application to the Control of Hard Disk Drives

• Published in: IEEE transactions on magnetics Vol. 45; no. 5; pp. 2140 - 2150
• Main Authors: Levin, J., Perez-Arancibia, N.O., Ioannou, P.A., Tsu-Chin Tsao
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive control systems; Adaptive disturbance rejection; Cross-disciplinary physics: materials science; rheology; Disk drives; Disturbances; Error correction; Exact sciences and technology; Feedforward; Frequency; hard disk drive; Hard disks; Magnetism; Materials science; Mathematical models; Neural networks; Other topics in materials science; Physics; Programmable control; Rail to rail outputs; real-time adaptive control; Reduction; Rejection; Studies; Tracking; USA Councils; Hard discs; Disc drives; Neural networks; hard disk drive; Adaptive disturbance rejection; Application; Adaptive control; Artificial intelligence; real-time adaptive control; Magnetic properties
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2009.2013339

This paper presents a neural-networks-based disturbance rejection adaptive scheme for dealing with repeatable and nonrepeatable runout simultaneously. The effectiveness of this method is demonstrated empirically on a commercial hard disk drive where the adaptive disturbance rejector is added to a baseline linear time-invariant (LTI) controller. The adaptive scheme can be broken into two subsystems: one subsystem is designed to suppress the repeatable runout (RRO) and the other to attenuate the residual disturbance and nonrepeatable runout (NRRO) by the use of radial basis functions. Two different methods for RRO suppression are employed in conjunction with the neural-networks-based NRRO rejector. The first one is an adaptive feedforward disturbance rejection scheme. The second is a repetitive controller. In both cases the neural modeled disturbance rejector is adapted online further increasing the track-following performance by as much as 6.4%. Experimental results of the schemes at various locations of the hard drive are included to demonstrate the general applicability of the approach on commercial drives. The total reduction of the error during track-following is measured to be as much as 25.4% respect to the baseline LTI controller.