RISE feedback control for a R/W head track following in hard disc drives

In this paper, the track following problem of the Read/Write (R/W) head of a Hard-Disc-Drive (HDD) is addressed using Robust Integral of Sign Error (RISE) based Neural Network (NN) technique. The proposed control scheme is required to compensate as much as possible the nonlinear hysteresis friction behavior which degrades the HDD performance through generating important residual tracking errors. It is well shown that the RISE technique, along with the NN based feedforward control, is able to guarantee the stability of such a system. Moreover, the boundedness of the closed-loop signals is ensured. To the best authors' knowledge, the suggested control solution, applied at the low frequency region of a HDD, has never been conducted before on such system. Different simulation scenarios are performed including nominal case and external disturbance rejection to demonstrate that the proposed solution is robust and efficient to achieve good tracking performances.

[1]  Bin Yao,et al.  Modeling and cancellation of pivot nonlinearity in hard disk drives , 2002 .

[2]  Jan Swevers,et al.  An integrated friction model structure with improved presliding behavior for accurate friction compensation , 1998, IEEE Trans. Autom. Control..

[3]  Masayoshi Tomizuka,et al.  Pivot Friction Compensation Using an Accelerometer and a Disturbance Observer for Hard Disk Drives , 1997, 8th International Symposium on Information Storage and Processing Systems.

[4]  Carlos Canudas de Wit,et al.  A new model for control of systems with friction , 1995, IEEE Trans. Autom. Control..

[5]  D. Abramovitch,et al.  Disk Drive Pivot Nonlinearity Modeling Part I : Frequency Domain , 1994 .

[6]  Terril Hurst,et al.  Disk drive pivot nonlinearity modeling. II. Time domain , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[7]  Feei Wang Disk Drive Pivot Nonlinearity Modeling Part II : Time Domain , 1994 .

[8]  Xuemei Ren,et al.  Feedforward Control Based on Neural Networks for Hard Disk Drives , 2009, IEEE Transactions on Magnetics.

[9]  W. Dixon,et al.  Lyapunov-based tracking control in the presence of uncertain nonlinear parameterizable friction , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  Shuzhi Sam Ge,et al.  Adaptive Neural Network Control of Hard Disk Drives With Hysteresis Friction Nonlinearity , 2011, IEEE Transactions on Control Systems Technology.

[11]  Jian Chen,et al.  A continuous asymptotic tracking control strategy for uncertain nonlinear systems , 2004, IEEE Transactions on Automatic Control.

[12]  Guoqiang Hu,et al.  Lyapunov-Based Tracking Control in the Presence of Uncertain Nonlinear Parameterizable Friction , 2007, IEEE Transactions on Automatic Control.

[13]  K. Åström Revisiting the LuGre Model Stick-slip motion and rate dependence , 2011 .

[14]  Warren E. Dixon,et al.  Asymptotic Tracking for Systems With Structured and Unstructured Uncertainties , 2006, IEEE Transactions on Control Systems Technology.

[15]  Li Li,et al.  Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case , 2007, IEEE Transactions on Industrial Electronics.

[16]  Faa-Jeng Lin,et al.  Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism , 2006, IEEE Transactions on Neural Networks.

[17]  K.J. Astrom,et al.  Revisiting the LuGre friction model , 2008, IEEE Control Systems.

[18]  Carlos Canudas de Wit,et al.  A survey of models, analysis tools and compensation methods for the control of machines with friction , 1994, Autom..

[19]  Xiong Liu,et al.  Analysis and measurement of torque hysteresis of pivot bearing in hard disk drive applications , 1999 .

[20]  Bin Yao,et al.  Modeling and cancellation of pivot nonlinearity in hard disk drive , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[21]  W. Marsden I and J , 2012 .

[22]  Warren E. Dixon,et al.  Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure , 2008, IEEE Transactions on Automatic Control.

[23]  A. Ramasubramanian,et al.  Adaptive friction compensation using extended Kalman-Bucy filter friction estimation: a comparative study , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[24]  Béla Lantos,et al.  Modeling, Identification, and Compensation of Stick-Slip Friction , 2007, IEEE Transactions on Industrial Electronics.

[25]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.