Machines Predictive Control

Dear Colleagues,

In recent years, Model Predictive Control (MPC) has been a powerful advanced control technology in industrial machine drives, due to its superior control performance, excellent dynamic response and the ability to easily include multiple-objective control into the cost function. At each sampling time, MPC defines the control action by minimizing a cost function that describes the desired machine behavior. This cost function compares the predicted variables to be controlled with their references. The predicted variables are calculated from the machine model and duplicated according to the possible voltage vectors of the power converter.

In classical MPC, much attention has been paid to the control performance, through the development of several control techniques for the different power converters topologies and machines. However, there are still some issues that constitute an open topic for research. Despite the huge progress of MPC for electrical machines, the control stability and robustness under harsh operating conditions, as well as the formal way of selecting optimally the weighting factor in the cost function considering the multi-objective control, are topics of interest that require further investigation. Nowadays, with the increasing complexity of power converters and machines, the independence from the model and parameters that may change with the operating point and environment, as well as the reduction of the excessive computational burden due to the duplicated prediction have a significant impact on the machine performance and drive cost.

The aim of this Special Issue is to provide an opportunity for scientists, researchers, and practicing engineers to share and disseminate their latest discoveries and results in the aforementioned fields, indicating the future trends for machines predictive control.

Topics include, but are not limited to, the following research areas:

• New MPC of electrical machines
• Stability and robustness of MPC
• Model-free predictive control approaches
• MPC algorithms with reduced computational complexity
• Implementation issues of MPC
• Artificial intelligence and data-driven in predictive control

Prof. Dr. Antonio J. Marques Cardoso
Dr. Imed Jlassi
Guest Editors

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