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An Indirect Adaptive Approach to Reject Multiple Narrow-Band Disturbances in Hard Disk Drives

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This paper presents an indirect adaptive control scheme that rejects unknown multiple narrow-band disturbances in hard disk drive systems. The proposed algorithm first finds the model of the disturbance (the internal model) and then adaptively estimates its parameters. The design of a band-pass filter with multiple narrow pass-bands is then presented and used to construct a disturbance observer (DOB) for disturbance rejection. The proposed algorithm estimates the minimal amount of parameters, and is computationally simple. Evaluation of the proposed algorithm is performed on a benchmark problem for HDD track following.
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... Alternatively, the disturbance frequency can be firstly estimated and then applied for controller design. This indirect-adaptive-control perspective has been considered in [9]- [11]. Indeed, frequency identification of narrow-band signals is a problem that receives great research attention itself. ...
... Algorithm tuning: besides the above structural mapping between the identified parameters and the vibration frequency, another property is also useful for algorithm tuning: the width of the attenuation bandwidth in Fig. 3 is determined by the parameter α in (11). When α is smaller but very close to one, the magnitude of Q(z −1 ) is very small except at the vibration frequencies. ...
... The first subplot is the time-domain result corresponding to the spectra in Fig. 6; and the second subplot is another example with step frequency changes in the disturbance. The third one is for the case when the disturbance is a chirp signal whose frequency sweeps between 50 Hz and 95 Hz in the time windows { [10][11][12][13][14][15], [20][21][22][23][24][25]} sec. Under such time-varying vibrations, we see that the proposed algorithm maintains its effectiveness of compensating the disturbance. ...
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This paper presents an adaptive control scheme for identifying and rejecting unknown and/or time-varying narrow-band vibrations. We discuss an idea of safely and adaptively inverting a (possibly non-minimum phase) plant dynamics at selected frequency regions, so that structured disturbances (especially vibrations) can be estimated and canceled from the control perspective. By taking advantage of the disturbance model in the design of special infinite-impulse-response (IIR) filters, we can reduce the adaptation to identify the minimum amount of parameters, achieve accurate parameter estimation under noisy environments, and flexibly reject the narrow-band disturbances with clear tuning intuitions. Evaluation of the algorithm is performed via simulation and experiments on an active-suspension benchmark.
... Alternatively, the disturbance frequency can be firstly estimated and then applied for controller design. This indirect-adaptive-control perspective has been considered in [8]- [10]. ...
... Additional filtering can be applied on w 1 to improve the signalto-noise ratio. See, e.g.,[10]. ...
... Xu et. al [8][9][10][11] introduced a minimum-parameter adaptive Q filter in DOB and extended it to multiple-band cases, where disturbances with multiple spectral peaks can be accurately estimated and effectively rejected. ...
... Equation (9) suggests that minimizing e(k) without changing S 0 (z −1 ) is equivalent to minimizing (1 − q −m Q(q −1 ))e b (k), where e b (k) = P(q −1 )S 0 (q −1 )d(k) reflects the baseline PES. ...
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... One specifies a dynamic model of these in the closed-loop system as the primary measure to settle the problem. In this scheme, the internal model principle (IMP) aids in performing an indirect adaptive control as done by Chen and Tomizuka [18]. Once a disturbance model is met, the parameters can be tuned. ...
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Chapter
The sections in this article are1The Problem2Background and Literature3Outline4Displaying the Basic Ideas: Arx Models and the Linear Least Squares Method5Model Structures I: Linear Models6Model Structures Ii: Nonlinear Black-Box Models7General Parameter Estimation Techniques8Special Estimation Techniques for Linear Black-Box Models9Data Quality10Model Validation and Model Selection11Back to Data: The Practical Side of Identification
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