Robust control of DC-DC converter supplying constant power load with Finite-Set Model Predictive Control

Sorouri, Hoda; Sedighizadeh, Mostafa

doi:10.1109/pedstc53976.2022.9767487

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The main drawback of SMC is that it is challenging to impose restrictions or control abstract quantities. To cover these drawbacks, FCS-MPC has been identified as one of the most favorable controllers for power electronic applications due to its capability over real-time solutions to multiple objectives and constraints [17,18]. The performance of FCS-MPC is deeply influenced by the weighting coefficients, the tuning of which is still a challenge to be undertaken.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

et al. 2022

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This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including preventing the dependency of the converter control system on the operating point conditions, plug-and-play capability, and robustness against the MG uncertainties and unknown load dynamics.

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Section: Introductionmentioning

confidence: 99%

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

et al. 2022

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“…5shows real-time experimental setup. The performance of the proposed ANN-based Hybrid controller is studied under two different scenarios, and compared with a backstepping controller [18] and a Hybrid controller with constant gains. In the Hybrid controller design without ANN, the control parameters are considered as K T = 10, K I = 20, and K D = 0.5.…”

Section: B Ann-based Intelligent Tuning Schemementioning

confidence: 99%

An Intelligent Hybrid Fractional Order Controller for DC–DC Buck Converters Feeding Constant Power Loads

Sorouri,

Oshnoei,

Oshnoei

et al. 2023

2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe)

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This paper presents an intelligent hybrid fractional order controller for regulating the output voltage of a DC-DC buck converter feeding constant power loads. The controller combines tilt integral derivative and fractional order proportional integral derivative controllers. An artificial neural network is used for online adaptation of the controller gains to avoid the dependence on the response of the converter control system on operating point conditions. Real-time simulations demonstrate the effectiveness of the proposed method in regulating the output voltage and mitigating the impact of varying power loads. The proposed controller outperforms a backstepping controller and a hybrid controller with constant gains.

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A Generalized Dynamic Nonsmooth Control Design for DC Microgrids based on Deep Reinforcement Learning

Zhou,

Zhang,

et al. 2023

2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)

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Robust control of DC-DC converter supplying constant power load with Finite-Set Model Predictive Control

Cited by 4 publications

References 26 publications

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

An Intelligent Hybrid Fractional Order Controller for DC–DC Buck Converters Feeding Constant Power Loads

A Generalized Dynamic Nonsmooth Control Design for DC Microgrids based on Deep Reinforcement Learning

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