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Constrained Model Predictive Control for dc-dc Buck Power Converters K. Gaouzi 1 , H. El Fadil 2 , A. Rachid 3 , F.Z. Belhaj 4 , F. Giri 5 1,2,3,4 ESIT Team, LGS Laboratory ENSA, Ibn Tofail University, 14000 Kénitra, Morocco. 5 Laboratoire d’Automatique de Caen, Université de Caen, Bd Marechal Juin, B.P 8156, 14032, Caen, France. 1 khawla.gaouzi@gmail.com, 2elfadilhassan@yahoo.fr, 3 rachidaziz03@gmail.com, 4 fz.blhj@gmail.com, 5 giri@greyc.ensicaen.fr Abstract— This paper proposes a Model Predictive Control (MPC) of dc-dc buck power converter. The control objective is to ensure a tight regulation of the output voltage and asymptotic stability of the closed loop system. It is shown using simulations results that the output voltage perfectly tracks its reference. Keywords— DC-DC Buck converter, MPC, Constraints, quadratic programming, Matlab. I. INTRODUCTION During the past few decades, power converters have attracted great interest in both automatic and electrotechnics field. They are devices that provide a supply to the electric machines by the conversion of an electrical signal. DC-DC converters are some of the most important circuits within the family of power circuits. They are largely used in power supplies for electronic equipment to control the energy flow between two DC systems. Due to their intrinsic nonlinearity, these systems represent an interesting field for control algorithms [1]. There are many control strategies available for the control of these processes.1 Model Predictive Control (MPC) is an optimal control strategy based on numerical optimization with a variety of advantages. It predicts the future control inputs and future plant responses based on a system model, which is optimized at regular interval. MPC take control actions by anticipating future events [2]. MPC algorithms is very simple to design and implement, it can control large-scale systems with many control variables [3][4]. Predictive control presents several advantages that make it suitable for the control of power converters: Concepts are intuitive and easy to understand, it can be applied to a variety of systems, constraints and nonlinearities can be easily included, multivariable case can be considered, and the resulting controller is easy to implement ([5], [6], [7], [8], [9], [10]). It requires a high amount of calculations, compared to a classic control scheme; however, the fast microprocessors available today make possible the implementation of predictive control. Generally, the quality of the controller depends on the quality of the model.