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Neural Network-Based System Identification and Controller Synthesis for an Industrial Sewing Machine  

Kim, Il-Hwan (Department of Electrical and Computer Engineering, Kangwon National University)
Stanley Fok (Department of Electrical and Computer Engineering, University of Waterlo)
Kingsley Fregene (Department of Electrical and Computer Engineering, University of Waterlo)
Lee, Dong-Hoon (Department of Electrical and Computer Engineering, Kangwon National University)
Oh, Tae-Seok (Department of Electrical and Computer Engineering, Kangwon National University)
David W. L. Wang (Department of Electrical and Computer Engineering, University of Waterloo)
Publication Information
International Journal of Control, Automation, and Systems / v.2, no.1, 2004 , pp. 83-91 More about this Journal
Abstract
The purpose of this paper is to obtain an accurate nonlinear system model to test various control schemes for a motion control system that requires high speed, robustness and accuracy. An industrial sewing machine equipped with a Brushless DC motor is considered. It is modeled by a neural network that is configured as an output-error dynamical system. The identified model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a 2 degree-of-freedom PID controller to compensate the effects of disturbance without degrading tracking performance has been de-signed. In this experiment, it is not preferable for safety reasons to tune the controller online on the actual machinery. Experimental results confirm that the model is a good approximation of sewing machine dynamics and that the proposed control methodology is effective.
Keywords
2 DOF PID controller; genetic algorithm; neural network; system identification.;
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Times Cited By Web Of Science : 5  (Related Records In Web of Science)
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