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dc.titleIterative reference adjustment for high-precision and repetitive motion control applications
dc.contributor.authorTan, K.K.
dc.contributor.authorZhao, S.
dc.contributor.authorHuang, S.
dc.identifier.citationTan, K.K., Zhao, S., Huang, S. (2005-01). Iterative reference adjustment for high-precision and repetitive motion control applications. IEEE Transactions on Control Systems Technology 13 (1) : 85-97. ScholarBank@NUS Repository.
dc.description.abstractIn this paper, a learning control scheme is proposed which is suitable for high-precision and repetitive motion control applications. It comprises of a self-tuning radial basis function (RBF) network operating in parallel with an iterative learning control (ILC) component. Unlike the usual ILC scheme which adapts a feedforward control signal to achieve improved tracking performance over time, the proposed scheme iteratively adjusts the reference signal. The RBF network is employed as a nonlinear function estimator to model the tracking error over a cycle, and this error model is subsequently used implicitly in the iterative adaptation of the reference signal over the next cycle. The ILC component further enhances the tracking performance, particularly over the sections of the trajectory where the RBF network is less adequate in its modeling function. Simulation examples and real-time experimental results are fully furnished to elaborate the various highlights of the proposed method. © 2005 IEEE.
dc.subjectIterative learning control (ILC)
dc.subjectNonlinear systems
dc.subjectPermanent-magnet linear motors (PMLMs)
dc.subjectPrecision motion control
dc.subjectRadial basis function (RBF)
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleIEEE Transactions on Control Systems Technology
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