Hierarchical fuzzy control for C-axis of CNC turning centers using geneticalgorithms

A combined PD and hierarchical fuzzy control is proposed for the low-speed control of the C-axis of CNC turning centers considering the effects of tra nsmission flexibility and complex nonlinear friction. Learning of the hiera rchical structure and parameters of the suggested control strategy is carri ed out by using the genetic algorithms. The proposed algorithm consists of two phases: the first one is to search the best hierarchy, and the second t o tune the consequent center values of the constituent fuzzy logic systems into the hierarchy. For the least total control rule number, the hierarchic al fuzzy controller is chosen to include only the simple two-input/one-outp ut fuzzy systems, and both binary and decimal genes are used for the select ion, crossover and mutation of the genetic algorithm. The proposed approach is validated by the computer simulation. Each generation consists of 30 in dividuals: ten reproduced from its parent generation, ten generated by cros sover, and the other ten by mutation. In the simulations, the C-axis is ass umed to be driven by a vector-controlled AC induction motor, and the dynami c friction model suggested by Canudas de Wit et al. in 1995 is used.