Lyapunov-Based Fast Finite-State Model Predictive Control for Sensorless Three-Phase Four-Arm MMC

This article investigates a novel finite control set model predictive control (FCS-MPC) for sensorless three-phase four-arm modular multilevel converter (MMC). The main objectives of the proposed solution are to avoid the cumbersome online tuning of weighting factors and to achieve the balance of multiple control objectives, allowing a lower control complexity and associated costs. To be specific, a computationally efficient FCS-MPC with respect to a Lyapunov-based cost function formula, which has a good potential to flexibly achieve the balance of multiple control objectives and to avoid the selection of weighting factors, is first proposed. Next, an improved adaptive linear-neuron-based submodule voltage estimation together with arm-current sensorless sorting technique is incorporated into this proposal. The main contribution of the proposed methodology is to provide a simple and user-friendly solution to complex multiple control problems while allowing an improvement in computationally efficient optimization. Finally, comprehensive simulation and experimental results are provided to illustrate the efficacy of the proposal for three-phase four-arm MMC.