A prediction-based optimal gain selection in RISE feedback control for hard disk drive

This paper presents a prediction-based optimal gain selection in Robust Integral Sign of the Error (RISE) based Neural Network (NN) approach. Previous research has shown that combining a feedforward term with a feedback control element yields an asymptotically stable closed-loop system. The proposed...

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Main Authors: Taktak-Meziou, M., Chemori, A., Ghommam, J., Derbel, N.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Closed loop systems
Feedforward neural networks
Optimized production technology
Robustness
Target tracking
Uncertainty
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Chemori, A.
Ghommam, J.
Derbel, N.
description This paper presents a prediction-based optimal gain selection in Robust Integral Sign of the Error (RISE) based Neural Network (NN) approach. Previous research has shown that combining a feedforward term with a feedback control element yields an asymptotically stable closed-loop system. The proposed approach adds a prediction-based optimal technique which minimizes a quadratic performance index to calculate an optimal feedback gain. The resulting novel controller, called P-RISE-NN, is applied for a track following problem of a Hard-Disc-Drive servo-system. Simulation studies are used to show the efficiency of the proposed control scheme and its robustness against external disturbances and parametric uncertainties in the system. The authors believe that the proposed control solution combining RISE with a predictive control approach has never been conducted before.
doi_str_mv 10.1109/CCA.2014.6981615
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subjects Artificial neural networks
Closed loop systems
Feedforward neural networks
Optimized production technology
Robustness
Target tracking
Uncertainty
title A prediction-based optimal gain selection in RISE feedback control for hard disk drive
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