an indirect adaptive neuro-fuzzy speed control of induction motors
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abstract
this paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. the uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. the contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of induction motors. the online training of the neuro-fuzzy systems is based on the lyapunov stability analysis and the reconstruction errors of the neuro-fuzzy systems are compensated in order to guarantee the asymptotic convergence of the speed tracking error. moreover, to improve the control system performance and reduce the chattering, a pi structure is used to produce the input of the neuro-fuzzy systems. finally, simulation results verify high performance characteristics and robustness of the proposed control system against plant parameter variation, external load and input voltage disturbance.
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Journal title:
journal of ai and data miningPublisher: shahrood university of technology
ISSN 2322-5211
volume
issue Articles in Press 2015
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