Enhanced Dynamic Performance of Modular Multilevel Converters Using Exponential Cost Function-Based Model Predictive Control

Journal Article (2026)
Author(s)

S. Mishra (Indian Institute of Technology (IIT))

S. Singh (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Subir Das (Indian Institute of Technology (IIT))

A. Lekić (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Intelligent Electrical Power Grids
DOI related publication
https://doi.org/10.1109/ACCESS.2026.3689159 Final published version
More Info
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Publication Year
2026
Language
English
Research Group
Intelligent Electrical Power Grids
Journal title
IEEE Access
Volume number
14
Pages (from-to)
67967-67990
Downloads counter
37
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Abstract

This paper proposes the use of Model Predictive Control (MPC) with an exponential cost function for the Modular Multilevel Converter (MMC), which is widely recognized as a preferred converter topology for integrating and converting renewable energy sources into electrical energy. MPC provides a superior control strategy in the presence of system constraints, a straightforward control design, facilitates the inclusion of multiple control objectives through a flexible cost-function formulation, and offers excellent control performance. By formulating an appropriate cost function, an MMC’s operational goals can be effectively achieved through MPC. However, non-exponential MPC approaches typically employ a rectangular moving-horizon window whose length matches the chosen prediction horizon, which can affect closed-loop stability. The results based on the non-exponential cost further reveal that the choice of prediction horizon notably influences the numerical conditioning of MPC algorithms. In particular, as the prediction horizon lengthens, the numerical condition tends to degrade rapidly when a large control horizon is used. This research work uses an exponentially weighted moving horizon window to overcome these issues. Employing the exponential-based cost function further significantly reduces the condition number of the Hessian matrix, thereby improving the numerical properties of the MPC. We further analyze the effects of different constraints, observing that the MPC strictly adheres to them and that the control variable influences the response of the MMC plant’s performance. We further compared our results with those of other controllers and analyzed performance metrics, demonstrating that the exponential MPC is effective in this case. Additionally, the results presented in this paper demonstrate the prescribed degree of stability and highlight the importance of fine-tuning key MPC parameters for the MMC model. The exponential-based MPC is validated for the MMC under scenarios involving small and large active and reactive power disturbances, considering offline simulations.