Model Predictive Control of Electrical Systems: Methods, Results, and Challenges

Dear Colleagues,

Recently, Model Predictive Control (MPC) methods have gained considerable attention due to their straightforward design, simple inclusion of different objectives and their discrete nature that is natural for the control of power converters. Therefore, it has been designed for a lot of different converter topologies, targeting a wide range of applications. The basic idea behind this technique is using the system’s mathematical model to predict its future behavior according to the different switching states. By solving an optimization problem that includes the control objectives, the predicted variables, and the possible constraints of the system, control actions are applied. However, MPC’s design and implementation can impose some limitations due to the high computational burden and/or variable switching frequency operation and/or weighting factors design. Improvements to MPC algorithms and their combination with intelligent controllers have been proposed. Therefore, this Special Issue concentrates on the latest advancements in model predictive control algorithm design.

This Special Issue on “Model Predictive Control of Electrical Systems: Methods, Results, and Challenges” seeks high-quality works focusing on Model Predictive Control applied to Electrical Systems. Topics of interest include, but are not limited to:

• Latest development in and improvements to predictive control algorithms.
• Recent model predictive control solutions for weighting factor elimination.
• New model predictive control methods for switching frequency reduction and control, and computation burden reduction.
• Model-free predictive control solutions for power converters in grid-connected and motor-drive applications.
• Model predictive control designs for power converters: multilevel converters, matrix converters, DC-DC, DC/AC and AC/DC.
• Model predictive control for grid connectivity applications: grid-tied converters, active front end rectifiers, etc.
• Model predictive control methods for power quality application: active filters, STATCOM, etc.
• Model predictive control for drive applications: induction motors, PMSM machines, etc.
• Hybrid control with model predictive techniques: MPC with artificial neural network ANN, MPC- fuzzy logic, MPC-sliding mode, etc.
• Machine learning with model predictive control.

Prof. Dr. Fernanda Carnielutti
Prof. Dr. Jose Rodriguez
Prof. Dr. Margarita Norambuena Valdivia
Guest Editors

Manuscript Submission Information

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• model predictive control
• electrical systems
• power electronics
• power systems
• microgrids
• machines and drives