Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input - delay systems

In this paper a new indirect type-2 fuzzy neural network predictive (T2FNNP) controller has been proposed for a class of nonlinear systems with input-delay in presence of unknown disturbance and uncertainties. In this method, the predictor has been utilized to estimate the future state variables of the controlled system to compensate for the time-varying delay. The T2FNN is used to estimate some unknown nonlinear functions to construct the controller. By introducing a new adaptive compensator for the predictor and controller, the effects of the external disturbance, estimation errors of the unknown nonlinear functions, and future sate estimation errors have been eliminated. In the proposed method, using an appropriate Lyapunov function, the stability analysis as well as the adaptation laws is carried out for the T2FNN parameters in a way that all the signals in the closed-loop system remain bounded and the tracking error converges to zero asymptotically. Moreover, compared to the related existence predictive controllers, as the number of T2FNN estimators are reduced, the computation time in the online applications decreases. In the proposed method, T2FNN is used due to its ability to effectively model uncertainties, which may exist in the rules and data measured by the sensors. The proposed T2FNNP controller is applied to a nonlinear inverted pendulum and single link robot manipulator systems with input time-varying delay and compared with a type-1 fuzzy sliding predictive (T1FSP) controller. Simulation results indicate the efficiency of the proposed T2FNNP controller.