Exponential Weighting Model Predictive Control with Observer for Modular Multilevel Converters
This work addresses control challenges for modular multilevel converters, offering improved dynamic performance and stability guarantees, but is incremental in nature.
The paper proposes an exponential weighting model predictive control with an observer for modular multilevel converters to improve dynamic performance and handle large prediction horizons. The method ensures constraint satisfaction and provides closed-loop stability guarantees.
In this article, we propose a model predictive control (MPC) scheme with an exponential cost function, along with an observer for the Modular Multilevel Converter (MMC), to enhance converter dynamic performance. In particular, as the prediction horizon $(N_P)$ increases, the numerical conditioning deteriorates rapidly, especially when a large $N_P$ is employed. This research work uses an appropriate cost function weighted to overcome the limitations of a large $N_P$. We further analyse the effects of constraints, observing that the designed MPC strictly adheres to them and that the control variable influences the MMC plant's response. The presence of the observer improves the prediction of the output, particularly for setpoint changes in the reference signal. We also analyze the prescribed performance, which provides a priori guarantees of closed-loop stability for the proposed controller.