The application of a fuzzy neural network controller for compensating the effects induced by the friction in a DC-motor micromaneuvering system is considered in this article. A back-propagation neural network is employed to decrease the effects of the system nonlinearities. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. A fuzzy cell space controller supervises the overall scheme and reduces the amplitude and repetitions of control switchings. Simulation studies are presented to indicate the effectiveness of the proposed algorithm.

Issue Section:
Technical Briefs
Topics:
Engines, Fuzzy neural nets, Motors, Control equipment, Artificial neural networks, Algorithms, Friction, Simulation
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