In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced. The proposed FO-MPC is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR). The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.
In this paper, we propose a new fractional-order adaptive generalized predictive control (FA-GPC) design based on a fractional order Romero GPC cost function and online estimation of the plant model using the Recursive Least Square (RLS) algorithm. The plant model is supposed to be linear time-invariant with unknown parameters. The proposed controller is applied in numerical simulation to a DC motor system and compared with the classical adaptive generalized predictive control (A-GPC). Simulation results illustrate the effectiveness of the proposed adaptive FA-GPC controller.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.