In this paper, two intelligent adaptive controllers for milling processes are proposed. One is an intelligent adaptive controller with optimization (IACO) developed based on a neural network and genetic algorithm. The other is an intelligentadaptive controller with constraints (IACC) developed based on a neural network and expert rules. In the IACO, a modified back-propagation neural network (MBPNN), in which a dynamic factor is attached and the learning rate can be adjusted in the learningprocess is used for the online modelling of the milling system. In addition, a modified genetic algorithm (MAG), in which the search domain can be adjusted in every generation is used for the real4ime optimal control of the milling process. In IACC, asimplified BP algorithm is used to learn online, the reverse function of the milling system and realize the real4ime adaptive control in the milling process; some expert rules are combined in the BP neural network controller so as to ensure thereliability and stability of the adaptive milling system. The experimental results show that not only does the milling system with the intelligent adaptive controllers have high robustness and global stability, but also the machining efficiency of themilling system with the intelligent adaptive controllers is much higher than the traditional CNC milling system.
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