Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems
Integrated Computer-Aided Engineering
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With the field-oriented method, the dynamic behavior of the induction motor is rather similar to that of a separately excited DC motor. However, the control performance of the induction motor is still influenced by the unmodelled dynamics or external disturbances, and to compensate for these uncertainties, adaptive fuzzy control is proposed. The overall control signal consists of two elements, (1) the equivalent control which is used for linearization of the induction motor’s model through feedback of the state vector. The equivalent control includes neurofuzzy approximators of the unknown parts of the induction motor model (2) the supervisory control which consists of an $$H_{\infty}$$ term and compensates for parametric uncertainties of the induction motor model and external disturbances. The performance of the proposed adaptive fuzzy $$H_{\infty}$$ controller is compared to backstepping nonlinear control through simulation tests.