Model predictive control and linear control of DC–DC boost converter in low voltage DC microgrid: An experimental comparative study

Guo, Qihao; Imen, Bahri; Berthelot, Éric

doi:10.1016/j.conengprac.2022.105387

Cited by 24 publications

(7 citation statements)

References 35 publications

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“…These approaches use two networks for separating Q-values and updating the actor's selection. The double Q-learning calculates the next state number, forming double Q-value networks as defined in Equations ( 16) and (17) [36].…”

Section: A Twin-delayed Deep Deterministic Policy Gradient (Td3)mentioning

confidence: 99%

“…Yet, dealing with converters will lead to complex problems because of nonlinear time variants as well as nonminimum phase characteristics [15]. In addition, the presence of load uncertainty can lead to instability in the system [16] and become more challenging to resolve [17]. To tackle these problems, several industries are currently implementing a proportional integral (PI) controller because of its straightforward configuration, reliability, simplicity of application, good performance,…”

Section: Introductionmentioning

confidence: 99%

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Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid

Muktiadji,

Ramli,

Milyani

2024

Electronics

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A stable output voltage of a boost converter is vital for the appropriate functioning of connected devices and loads in a DC microgrid. Variations in load demands and source uncertainties can damage equipment and disrupt operations. In this study, a modified twin-delayed deep deterministic policy gradient (TD3) algorithm is proposed to regulate the output voltage of a boost converter in a DC microgrid. TD3 optimizes PI controller gains, which ensure system stability by employing a non-negative, fully connected layer. To achieve optimal gains, multi-deep reinforcement learning agents are trained. The agents utilize the error signal to obtain the desired output voltage. Furthermore, a new reward function used in the TD3 algorithm is introduced. The proposed controller is tested under load variations and input voltage uncertainties. Simulation and experimental results demonstrate that TD3 outperforms PSO, GA, and the conventional PI. TD3 exhibits less steady-state error, reduced overshoots, fast response times, fast recovery times, and a small voltage deviation. These findings confirm TD3’s superiority and its potential application in DC microgrid voltage control. It can be used by engineers and researchers to design DC microgrids.

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Section: A Twin-delayed Deep Deterministic Policy Gradient (Td3)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid

Muktiadji,

Ramli,

Milyani

2024

Electronics

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“…A widely used nonlinear controller, the model predictive controller (MPC), is utilized to minimize a cost function and generate control signals for converters [15,16]. In [17], an MPC scheme was proposed to address the impact of pulse loads in a DC microgrid.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a High-Order Sliding Mode Controller for Photovoltaic- and Battery Energy Storage System-Based DC Microgrids with ANN-MPPT

Roy,

Oo,

Ghosh

2024

Energies

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This paper introduces a robust proportional integral derivative higher-order sliding mode controller (PID-HOSMC) based on a double power reaching law (DPRL) to enhance large-signal stability in DC microgrids. The microgrid integrates a solar photovoltaic (SPV) system, an energy storage system (ESS), and DC loads. Efficient DC-DC converters, including bidirectional and boost converters, are employed to maintain a constant voltage level despite the lower SPV output power. An artificial neural network (ANN) generates the optimal reference voltage for the SPV system. The dynamical model, which incorporates external disturbances, is initially developed and based on this model, and the PID-HOSMC is designed to control output power by generating switching gate pulses. Afterwards, Lyapunov stability theory is used to demonstrate the model’s closed-loop stability, and theoretical analysis indicates that the controller can converge tracking errors to zero within a finite time frame. Finally, a comparative numerical simulation result is presented, demonstrating that the proposed controller exhibits a 58% improvement in settling time and an 82% improvement in overshoot compared to the existing controller. Experimental validation using processor-in-the-loop (PIL) confirms the proposed controller’s performance on a real-time platform.

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“…According to Eq.1, the duty cycle of VT is defined as the conduction time (ton) of VT divided by the entire amount of time [8][9].…”

Section: Principle Of the Boost Converter Circuitmentioning

confidence: 99%

Research on maximum power point tracking of PV systems based on perturbation and observation method

Zhan,

Zhou,

Zhu

et al. 2023

International Conference on Electronic Materials and Information Engineering (EMIE 2023)

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Model predictive control and linear control of DC–DC boost converter in low voltage DC microgrid: An experimental comparative study

Cited by 24 publications

References 35 publications

Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid

Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid

Designing a High-Order Sliding Mode Controller for Photovoltaic- and Battery Energy Storage System-Based DC Microgrids with ANN-MPPT

Research on maximum power point tracking of PV systems based on perturbation and observation method

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