Abstract
In this paper a type-2 fuzzy RBFN-based sliding mode control is used for a class of nonlinear systems. A new recurrent type-2 fuzzy radial basis neural network (we named RT2FRBFN) is used to approximate the conventional sliding mode control law. This method does not require prior information about the system; therefore, simultaneously, RT2FRBFN detects the system's dynamics, as well as the estimated dynamics in the sliding mode controller. Finally, the proposed RT2FRBFN sliding mode control system is used to tracking control design with regard to uncertainty in a class of nonlinear systems. Combination of backstepping method and sliding mode control helps to compensate the control signal and get a better performance. The backstepping method is used to improve the final threshold stability and use the sliding mode control to obtain high speed and unchangeable response to uncertainty. Simulation results show the proposed RT2FRBFN-based sliding mode control has better performance than RBFN-based sliding mode control in the presence of uncertainty.
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