Prediction interval-based ANFIS controller for nonlinear processes

Prediction interval (PI) has been appeared as a promising tool to quantify the uncertainties and disturbances associated with point forecasts. Despite of its numerous applications in prediction problems, the use of PIs in control application is still limited. In this paper, a PI-based ANFIS controller is proposed and designed for nonlinear systems. In the proposed algorithm, a PI-based neural network model (PI-NN) is developed to construct the PIs, and this model is used as an online estimator of PIs for the controller. The PIs along with other traditional inputs are used to train the inverse ANFIS model. The developed PI-based ANFIS model is then used as a nonlinear PI-based controller (PIC). The performance of the proposed PIC is examined for a nonlinear numerical plant. Simulation results revealed that the proposed PIC performance is superior over the traditional ANFIS-based controller.

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