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|Title:||Iterative reference adjustment for high precision and repetitive motion control applications||Authors:||Tan, K.K.
|Keywords:||Iterative learning control
Permanent magnet linear motors
Precision motion control
Radial basis function (RBF) neural network
|Issue Date:||2002||Citation:||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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