摘要
为了克服模糊控制动态响应慢和鲁棒性差的缺点,将模糊控制的定性知识表达能力与小波分析优异的局部控制性能和神经网络的定量学习能力相结合,提出了一种模糊小波神经网络自适应控制器,并将其应用于加工过程控制。对变切削深度的铣削加工过程控制的仿真结果表明,基于模糊小波神经网络的加工过程自适应控制,其控制效果优于一般的模糊控制和神经网络控制,具有很好的动、静态性能。该自适应控制器能有效防止刀具损坏和提高加工效率,是一种有效的加工过程控制方法。
In order to improve the slow response and weak robustness of fuzzy control, by combining qualitative knowledge expressiveness of fuzzy control with excellent local property of wavelet analysis and quantitative learning ability of neural network, a fuzzy wavelet neural network controller is presented and is used to machining process control. Simulation results under varying cutting conditions show that the proposed controller is superior to the fuzzy control and the neural network control for machining process and it has better static, dynamic performance. This controller can improve the machining efficiency and protect the tool, and it is an effective method of machining process control.
出处
《工具技术》
北大核心
2008年第4期71-74,共4页
Tool Engineering
基金
广东省自然科学基金资助项目(项目编号:06025546)
珠海市科技计划项目(项目编号:PC20061040)
