硬盘驱动器RISE反馈控制中基于预测的最优增益选择

M. Taktak-Meziou, A. Chemori, J. Ghommam, N. Derbel
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引用次数: 5

摘要

本文提出了一种基于预测的鲁棒积分误差(RISE)神经网络的最优增益选择方法。以往的研究表明,将前馈项与反馈控制元件相结合可以得到一个渐近稳定的闭环系统。该方法增加了一种基于预测的优化技术,该技术将二次性能指标最小化以计算最优反馈增益。将所得到的新型控制器P-RISE-NN应用于硬盘驱动伺服系统的轨迹跟踪问题。仿真研究表明了所提控制方案的有效性以及对系统外部干扰和参数不确定性的鲁棒性。作者认为,提出的将RISE与预测控制方法相结合的控制方案以前从未进行过。
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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 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.
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