Model Predictive Control of DC–DC SEPIC Converters With Autotuning Weighting Factor

Güler, Naki; Biricik, Samet; Bayhan, Sertaç; Kömürcügil, Hasan

doi:10.1109/tie.2020.3026301

Cited by 35 publications

(24 citation statements)

References 21 publications

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“…Since all calculations required to accurately determine the optimal weighting factor are done online, these techniques generally increase the computational burden of the predictive control process. Methods employed in the literature include: tracking error optimization [7], [28], [29], [66]- [68], torque ripple optimization [69], [70], coefficient of variation [71], state normalization/variable sensitivity balance [72], look-up table [73], [74], grey relational analysis [75] and continuous function of pre-existing error [76]. To facilitate the optimal weight factor calculation for predictive torque and flux control in particular, algebraic methods are presented in [69], [77], and these are not computationallyintensive.…”

Section: B Non-ai-based Online Optimizationmentioning

confidence: 99%

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

Babayomi¹,

Zhang²,

Yu³

et al. 2021

Preprint

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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.

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Section: B Non-ai-based Online Optimizationmentioning

confidence: 99%

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

Babayomi¹,

Zhang²,

Yu³

et al. 2021

Preprint

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“…Los factores de peso son agregados debido a que muchas veces la naturaleza de las variables en una función de costo son de unidades diferentes o de orden de magnitud diferente [6]. Los factores de peso son ajustados para determinar la importancia del término en la función de costo, siendo un desafío al momento de poner en marcha el control predictivo [7]- [10].…”

Section: Nomenclatura φSnunclassified

“…Algunas técnicas de diseño de los factores de pesos investigados últimamente son: auto-ajuste [11], ajuste de forma empírica [6], ajuste por medio de un algoritmo enjambre de partículas "Particle Swarm" [12] [13], ajuste por reconfiguración dinámica [14], diseño por medio de una red neuronal artificial [15] y diseño por medio de un algoritmo multiobjetivo heurístico [16], entre otros.…”

Section: Nomenclatura φSnunclassified

A study of weighting factor design in model predictive control applications

Rojas¹,

Rivera²,

Muñoz³

et al. 2021

2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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Weighting factor selection is considered to be a crucial aspect in the design of a predictive controller. In this paper a study of the design of weighting factors for different cost functions is presented. The study is based on three examples: switching frequency reduction, voltage vector reduction of the voltage source inverter and capacitor balancing of the three level neutral point clamped converter. The three examples have been evaluated using simulations in Matlab/Simulink which showed that by varying the weighting factor, there can be a negative or positive effect on the different system variables. Therefore the proper selection will depend on the system requirements, something which can now be defined.

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“…The main reason for this deterioration is the weighting factor, which is not adjusted for the new operating point. Therefore, adaptive weighting factor tuning methods are essential [22]. Some unknown variables such as lower and higher limits are needed in many adaptive tuning strategies [23].…”

Section: Introductionmentioning

confidence: 99%

An enhanced finite control set model predictive control method with self‐balancing capacitor voltages for three‐level T‐type rectifiers

Bayhan

Kömürcügil

Güler

2022

IET Power Electronics

Self Cite

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An effective finite control set model predictive control (FCS‐MPC) is introduced for single‐phase three‐level T‐type rectifiers supplying resistive as well as constant power loads (CPL). The main problem of CPL is the negative resistance phenomenon that endangers the rectifier's stability. Hence, the proposed FCS‐MPC method is based on Lyapunov's stability theory such that the stability of the rectifier is guaranteed under all operating points. Unlike the existing FCS‐MPC methods, the cost function design in the proposed control method is formulated on the rectifier's stability. According to Lyapunov's stability theory, the rectifier stays stable provided that the rate of change of Lyapunov function is negative. In this case, the derivative of the Lyapunov function can be used as the cost function without utilizing any weighting factor. Therefore, contrary to the existing FCS‐MPC methods, the weighting factor requirement is eliminated which leads to easiness in the design and implementation of the controller. Experimental results reveal that the proposed control approach exhibits very good performance with undistorted and distorted grid voltage conditions when the rectifier feeds resistive and CPL loads.

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Model Predictive Control of DC–DC SEPIC Converters With Autotuning Weighting Factor

Cited by 35 publications

References 21 publications

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

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

A study of weighting factor design in model predictive control applications

An enhanced finite control set model predictive control method with self‐balancing capacitor voltages for three‐level T‐type rectifiers

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