Massoud Hemmasian Ettefagh - Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
José De Doná - School of Electrical Engineering and Computing, The University of Newcastle, Australia
Mahyar Naraghi - Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Farzad Towhidkhah - Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

Kautz parametrization of the Model Predictive Control (MPC) method has shown its ability to reduce the number of decision variables in Linear Time Invariant (LTI) systems. This paper devotes to extend Kautz network to be used in MPC Algorithm for linear time-varying systems. It is shown that Kautz network enables us to maintain a satisfactory performance while the number of decision variables are reduced considerably. Stability of the algorithm is studied under the framework of the optimal solution. The proposed method is validated by an illustrative example. In this regard, the performance of unconstrained systems as well as constrained ones is compared.

Model Predictive Control; Kautz Network; Time-Varying Systems