A powerful model predictive control via stability condition for direct matrix converter

This paper offered a novel approach based on model predictive control for a direct matrix converter with a long prediction horizon. A discrete-time model of the dynamical system is used to predict the future response of the control variables for pursuing the reference load current. The long prediction horizon can be used to improve the control performance but increase the computational complexity. In contrast to the classical finite control set model predictive control, the proposed scheme employed a control Lyapunov function in the control design to guarantee a stable closed-loop system. During each sampling interval, only the probable control switching states which meet the sufficient stability condition are considered for evaluating the optimization processes, leading to a dramatic reduction of the computation time. An extensive examination of the conventional model predictive control and the proposed method under some operational circumstances is conducted with Matlab software. The simulation and experimental results prove the effectiveness and feasibility of the proposed control strategy in terms of stability and reduced computational burden.