Adaptive Gain and Order Scheduling of Optimal Fractional Order PIlamdaDµ Controllers with Radial Basis Function Neural-Network

Gain and order scheduling of fractional order (FO) PI^λD^μ controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.