Adaptive control of machining process based on extended entropy square error and wavelet neural netw

• 作 者: 赖兴余 叶邦彦 李伟光 鄢春艳
• 机 构: School of Mechanical Engineering South China University of Technology,School of Mechanical Engineeri
• 出 处: Journal of Harbin Institute of Technology
• 发表日期: 2007年03期
• 关键词: wavelet entropy machining translating criterion searching scaling hidden adjusti

Combining information entropy and wavelet analysis with neural network,an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error(EESE)and wavelet neural network(WNN).Extended entropy square error function is defined and its availability is proved theoretically.Replacing the mean square error criterion of BP algorithm with the EESE criterion,the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter,translating parameter of the wavelet and neural network weights.Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network.The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions,thus improving the machining efficiency and protecting the tool.

Combining information entropy and wavelet analysis with neural network, an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error (EESE) and wavelet neural network (WNN). Extended entropy square error function is defined and its availability is sure theoretically. Replacing the mean square error criterion of BP algorithm with the EESE criterion, the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self-adjusting scaling parameter , translating parameter of the wavelet and neural network weights. Simulation results show that the designed system is of fast response, non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network. suggested method can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions, thus improving the machining efficiency and protecting the tool.