@article { author = {Vahedi, M. and Hadad Zarif, M. and Akbarzadeh Kalat, A.}, title = {An indirect adaptive neuro-fuzzy speed control of induction motors}, journal = {Journal of AI and Data Mining}, volume = {4}, number = {2}, pages = {243-251}, year = {2016}, publisher = {Shahrood University of Technology}, issn = {2322-5211}, eissn = {2322-4444}, doi = {10.5829/idosi.JAIDM.2016.04.02.13}, 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.}, keywords = {indirect adaptive control,neuro-fuzzy approximators,uncertainty estimation,Stability analysis,reconstruction error}, url = {https://jad.shahroodut.ac.ir/article_526.html}, eprint = {https://jad.shahroodut.ac.ir/article_526_f96b03fadfcd7adc0da6311d369ee9de.pdf} }