Multivariable structure of fuzzy control systems
IEEE Transactions on Systems, Man and Cybernetics
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy Sets and Systems - Special issue on fuzzy neural control
An introduction to genetic algorithms
An introduction to genetic algorithms
A course in fuzzy systems and control
A course in fuzzy systems and control
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Genetic Algorithms for Machine Learning
Genetic Algorithms for Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fuzzy-enhanced Adaptive Control for Flexible Drive System with Friction Using Genetic Algorithms
Journal of Intelligent and Robotic Systems
Modeling and Hierarchical Neuro-Fuzzy Control for Flexure-Based Micropositioning Systems
Journal of Intelligent and Robotic Systems
Embedded fuzzy-control system for machining processes results of a case study
Computers in Industry
Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms
Journal of Intelligent and Robotic Systems
Embedded fuzzy control system: application to an electromechanical system
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
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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 transmission flexibility and complex nonlinear friction. Learning of the hierarchical structure and parameters of the suggested control strategy is carried 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 to tune the consequent center values of the constituent fuzzy logic systems into the hierarchy. For the least total control rule number, the hierarchical fuzzy controller is chosen to include only the simple two-input/one-output fuzzy systems, and both binary and decimal genes are used for the selection, crossover and mutation of the genetic algorithm. The proposed approach is validated by the computer simulation. Each generation consists of 30 individuals: ten reproduced from its parent generation, ten generated by crossover, and the other ten by mutation. In the simulations, the C-axis is assumed to be driven by a vector-controlled AC induction motor, and the dynamic friction model suggested by Canudas de Wit et al. in 1995 is used.