Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCST.2004.838549
Title: Iterative reference adjustment for high-precision and repetitive motion control applications
Authors: Tan, K.K. 
Zhao, S. 
Huang, S. 
Keywords: Iterative learning control (ILC)
Nonlinear systems
Permanent-magnet linear motors (PMLMs)
Precision motion control
Radial basis function (RBF)
Issue Date: Jan-2005
Source: Tan, 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. https://doi.org/10.1109/TCST.2004.838549
Abstract: In 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.
Source Title: IEEE Transactions on Control Systems Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/56431
ISSN: 10636536
DOI: 10.1109/TCST.2004.838549
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