Adaptive Generalized Predictive Control of Fractional Order Thermal Aluminum Rod

This paper presents the Adaptive Generalized Predictive Control (AGPC) of the heat conduction in a given aluminum rod modeled using fractional calculus. The oustaloup approximation is used to perform the integer model of the temperature of the rod, then a balanced truncation (BT) model order reduction technique is used to obtain a low integer order model used as internal prediction model of the predictive controller. In order to increase the performance metrics of the AGPC controller, the Improved Grey Wolf Optimizer (IGWO) is applied to obtain the best synthesis parameters which are the minimum prediction horizon, the maximum prediction horizon, the control horizon and the weighting factor of the control signal. Simulation results of IGWO-AGPC versus the GA based AGPC and the Ant Lion Optimizer based Fractional Order PIλDμ (ALO-FOPID) validates the effectiveness of the proposed approach.