Title: Neural network adaptive control for machining process based on generalised entropy square error and wavelet analysis
Authors: XingYu Lai, ChunYan Yan, BangYan Ye
Addresses: Department of Mechatronical Engineering, Guangdong Institute of Science and Technology, 351 Kehua Street, Guangzhou, Guangdong 510640, PR China. ' Department of Mechatronical Engineering, Guangdong Institute of Science and Technology, 351 Kehua Street, Guangzhou, Guangdong 510640, PR China. ' School of Mechanical Engineering, South China University of Technology, Wushan Road, Guangzhou, Guangdong 510640, PR China
Abstract: To improve convergent speed of the neural network for machining process control, Generalised Entropy Square Error (GESE) function is defined and its availability is proved theoretically. Combining information entropy and wavelet analysis with neural network, a neural network adaptive control system is presented. Replacing the mean square error criterion of back propagation algorithm with the GESE criterion, an adaptive control algorithm is proposed. The proposed system is then applied to the online control of the cutting force by searching adaptively wavelet base function and self-adjusting scale parameter, translation parameter of the wavelet and weights of the network. The designed system is of fast response and less overshoot, and the suggested algorithm can tune adaptively the feed rate online till achieving a constant cutting force approaching the reference force in varied cutting conditions, thus raising machining efficiency and protecting tool. Finally, simulation and experiment examples are also given to demonstrate the effectiveness of the proposed system and algorithm.
Keywords: machining; process control; adaptive control; neural networks; GESE; generalised entropy square error; wavelet analysis; information entropy; cutting force; feedrate tuning; simulation.
International Journal of Manufacturing Technology and Management, 2009 Vol.17 No.3, pp.246 - 260
Published online: 21 Mar 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article