Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/70727
Title: Iterative reference adjustment for high precision and repetitive motion control applications
Authors: Tan, K.K. 
Zhao, S. 
Keywords: Iterative learning control
Permanent magnet linear motors
Precision motion control
Radial basis function (RBF) neural network
Tracking convergence
Issue Date: 2002
Source: Tan, K.K.,Zhao, S. (2002). Iterative reference adjustment for high precision and repetitive motion control applications. IEEE International Symposium on Intelligent Control - Proceedings : 131-136. ScholarBank@NUS Repository.
Abstract: In this paper, a new iterative learning control (ILC) scheme is proposed which is suitable for high precision and repetitive motion control applications. 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. To achieve a higher convergence rate, a Radial Basis Function (RBF) neural network is employed to model the tracking error over a cycle, and subsequently used implicitly in the iterative adaptation of the reference signal over the next cycle. Simulation examples are furnished to elaborate the various highlights of the proposed method.
Source Title: IEEE International Symposium on Intelligent Control - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/70727
Appears in Collections:Staff Publications

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