Matrix converters are a well-known class of direct AC-AC power converter topologies that can be used in applications, where compact volume and low weight are necessary. For good performance, special attention should be paid to the control scheme used for these converters. The model predictive control strategy is a promising, straightforward and flexible choice for controlling various different matrix converter topologies. This work provides a comprehensive study and detailed classification of several predictive control methods and techniques, discussing special capabilities they each add to the operation and control scheme for different matrix converter topologies. This study also considers the issues regarding the implementation of model predictive control strategies for matrix converters. This survey and comparison are intended to be a useful guide for solving the related drawbacks of each topology and to enable the application of this control scheme for matrix converters in practical applications.

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Due to discrete nature of power converters, finite control set model predictive control (FCS‐MPC) is considered as an attractive choice for these systems. Since this control technique provides advantages like improved dynamic performance and inclusion of several control objectives in a single cost function, it is especially appropriate for topologies like modular multilevel converters (MMCs), where multiple control goals, that is, output and circulating currents control, and cells' capacitor voltage balancing should be satisfied. Despite these advantages, huge computational burden and weighting factor selection are two serious obstacles. In this work, a simple two‐stage weighting factor selection scheme is suggested for FCS‐MPC applied to MMCs. At the first stage, some offline tests are conducted for different weighting factors; and a pre‐optimal value is selected, based on minimizing the total harmonic distortion (THD) of output current and root mean square (RMS) value of the circulating current. However, since optimal performance at different operating conditions is achieved by different weighting factors, the pre‐optimal value will be updated at the second stage by a simple online algorithm. Moreover, a technique is suggested for effectively decreasing the computational burden. Finally, by conducting several simulation and experimental tests, the satisfactory performance of proposed controller is validated, compared to fixed weighting factor FCS‐MPC.