Model Predictive Control of a Modular Multilevel Converter Considering Control Input Constraints

Model predictive control (MPC) usually suffers from high computational complexity when it comes to modular multilevel converters (MMCs). Some researchers have attempted to use a modulated approach to reduce the computational burden and improve the control performance. But these methods do not consider the actual physical limitations of the control system, and therefore the control performance degrades at high modulation indices or transients. To solve this problem, a modulated MPC with bound-constrained quadratic programming has been proposed. With this method, the optimal solution of the control problem can be obtained, ensuring a better control performance under high modulation index conditions or in transients. Finally, a comparative experiment with the conventional modulated MPC methods has been carried out. The experimental results validate that the proposed method can achieve superior performance when the MMC operates at high modulation index, transients, and low frequencies.