scholarly journals A Comparative Study on Weighting Factor Design Techniques for the Model Predictive Control of Power Electronics and Energy Conversion Systems

2021 ◽  
Author(s):  
Oluleke Babayomi ◽  
Yuzhe Zhang ◽  
yu li ◽  
Yongdu Wang ◽  
Zhen Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Energy Conversion ◽  
Power Electronics ◽  
Predictive Control ◽  
High Performance ◽  
Dynamic Performance ◽  
Power Converter ◽  
Weighting Factor ◽  
Factor Design ◽  
Design Techniques

During the past decade, the model predictive control (MPC) of power electronics and drives has witnessed significant advancements in both dynamic performance and range of applications. However, researchers still encounter challenges with the optimal design of weighting factors, and this lowers the capabilities derivable from MPC. This study first reviews the different weighting factor design techniques proposed in the literature for power electronics and electrical drives (applied to wind/solar energy conversion, microgrids, grid-connected converters and other high performance converter-based systems). They are grouped under heuristic, offline tuning, sequential, and online optimization methods. Next, the study provides real-time hardware-in-the-loop comparative results for the implementation of four weighting factor design techniques on a grid-connected two-level back-to-back power converter-based permanent magnet synchronous generator wind turbine system. Through these laboratory results, the advantages and limitations of the different weighting factor design methods are highlighted.

scholarly journals A Comparative Study on Weighting Factor Design Techniques for the Model Predictive Control of Power Electronics and Energy Conversion Systems

2021 ◽  
Author(s):  
Oluleke Babayomi ◽  
Yuzhe Zhang ◽  
yu li ◽  
Yongdu Wang ◽  
Zhen Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Energy Conversion ◽  
Power Electronics ◽  
Predictive Control ◽  
High Performance ◽  
Dynamic Performance ◽  
Power Converter ◽  
Weighting Factor ◽  
Factor Design ◽  
Design Techniques

During the past decade, the model predictive control (MPC) of power electronics and drives has witnessed significant advancements in both dynamic performance and range of applications. However, researchers still encounter challenges with the optimal design of weighting factors, and this lowers the capabilities derivable from MPC. This study first reviews the different weighting factor design techniques proposed in the literature for power electronics and electrical drives (applied to wind/solar energy conversion, microgrids, grid-connected converters and other high performance converter-based systems). They are grouped under heuristic, offline tuning, sequential, and online optimization methods. Next, the study provides real-time hardware-in-the-loop comparative results for the implementation of four weighting factor design techniques on a grid-connected two-level back-to-back power converter-based permanent magnet synchronous generator wind turbine system. Through these laboratory results, the advantages and limitations of the different weighting factor design methods are highlighted.

scholarly journals Sequential Model Predictive Control of Three-Phase Direct Matrix Converter

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 214 ◽  
Author(s):  
Jianwei Zhang ◽  
Margarita Norambuena ◽  
Li Li ◽  
David Dorrell ◽  
Jose Rodriguez
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Weighting Factor ◽  
Matrix Converter ◽  
Cost Functions ◽  
Computational Burden ◽  
Three Phase ◽  
The Matrix ◽  
Factor Design

The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required. Compared with the standard MPC, the computation burden is reduced because only the pre-selected switch states are evaluated in the second and subsequent sequential cost functions. In addition, the prediction model computation for the following cost functions is also reduced. Specifying the priority for control objectives can be achieved. A comparative study with traditional MPC is carried out both in simulation and an experiment. Comparable control performance to the traditional MPC is achieved. This controller is suitable for the MC because of the reduced computational burden. Simulation and experimental results verify the effectiveness of the proposed strategy.

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

State of the Art of Finite Control Set Model Predictive Control in Power Electronics

2013 ◽  
Vol 9 (2) ◽  
pp. 1003-1016 ◽  
Author(s):  
Jose Rodriguez ◽  
Marian P. Kazmierkowski ◽  
Jose R. Espinoza ◽  
Pericle Zanchetta ◽  
Haitham Abu-Rub ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Power Electronics ◽  
Predictive Control ◽  
State Of The Art ◽  
Finite Control ◽  
Control Set
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