Adaptive Control of Machining Process Based on Fuzzy Wavelet Neural Network and Generalized Entropy Square Error

In order to improve the slow response and weak robustness of fuzzy control for machining process, combining qualitative knowledge expressiveness of fuzzy control with excellent local property of wavelet analysis and quantitative learning ability of neural network, a new kind of fuzzy wavelet neural network controller (FWNNC) is presented and a generalized entropy square error (GESE) function is also defined. The FWNNC is then applied to the on-line control of the cutting force under variable cutting conditions. Simulation results show that the proposed controller is superior to the fuzzy control or the neural network control for machining process and it has better static, dynamic performance. Experimental examples are also given to demonstrate the effectiveness of the proposed controller.