Modeling and Simulation of Buck-Boost Converter with Voltage Feedback Control

Zhou, Xuelian; He, Qinming

doi:10.1051/matecconf/20153110006

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…is the difference between the input voltage V in , and the product of the inductor's current i L and the sum of the resistance when the switch is on R on and the resistance of the inductor r L , which is divided by the inductance L [17]. The change in the capacitor's voltage v c :…”

Section: A Dc-to-dc Buck-boost Convertermentioning

confidence: 99%

“…In the consecutive cycles, where the MOSFET is in the onstate, the capacitor then supplies energy to the load [17]. The state-space representation for when the MOSFET switch is in the on-state is given by:…”

Section: A Dc-to-dc Buck-boost Convertermentioning

confidence: 99%

“…The critic network is trained to predict the value function shown by Eqn. (17), which measures how good it is to be in a specific state s t . The actor-critic network aims to maximise the surrogate objective function:…”

Section: B Proximal Policy Optimisation Algorithmmentioning

confidence: 99%

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Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

2021

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The data-driven model predictive control (DDMPC) scheme is proposed to obtain fast convergence to a desired reference and to be utilised to mitigate the destabilising effects that a DC-to-DC buck-boost converter (BBC) with an active load experiences. The DDMPC strategy uses the observed state to derive an optimal control policy using a reinforcement learning (RL) algorithm. The employed Proximal Policy Optimisation (PPO) algorithm's performance is benchmarked against the PI controller. From the simulated results obtained using the MATLAB Simulink solver, the most robust methods for short settling time and stability were the hybrid methods. These methods take advantage of the short settling time provided by the PPO algorithm and the stability provided by the PI controller or the filtering mechanism over the transient time. The source code for this study is available on GitHub to support reproducible research in industrial electronics society.INDEX TERMS Adaptive control, data-driven model predictive control, DC-to-DC buck-boost converter, proximal policy optimisation, reinforcement learning.

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Section: A Dc-to-dc Buck-boost Convertermentioning

confidence: 99%

Section: A Dc-to-dc Buck-boost Convertermentioning

confidence: 99%

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Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

2021

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“…Figure 6 shows the actuator scheme, which adjusts the power in the boiler heating resistor by regulating the voltage with a DC-DC converter. This is a simplified model of the buck-boost converter [21], widely used for the analysis of its operation and control [22][23][24][25][26]. DC-DC converters can regulate the output voltage to the desired value from the switching of electronic devices, usually diodes and transistors.…”

Section: Heating Actuator (Boiler) Modelmentioning

confidence: 99%

Analysis and Comparison of Fuzzy Models and Observers for DC-DC Converters Applied to a Distillation Column Heating Actuator

Heras-Cervantes

Téllez-Anguiano

Anzurez-Marin

et al. 2020

MCA

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In this paper, as an introduction, the nonlinear model of a distillation column is presented in order to understand the fundamental paper that the column heating actuator has in the distillation process dynamics as well as in the quality and safety of the process. In order to facilitate the implementation control strategies to maintain the heating power regulated in the distillation process, it is necessary to represent adequately the heating power actuator behavior; therefore, three different models (switching, nonlinear and fuzzy Takagi–Sugeno) of a DC-DC Buck-Boost power converter, selected to regulate the electric power regarding the heating power, are presented and compared. Considering that the online measurements of the two main variables of the converter, the inductor current and the capacitor voltage, are not always available, two different fuzzy observers (with and without sliding modes) are developed to allow monitoring the physical variables in the converter. The observers response is compared to determine which has a better performance. The role of the observer in estimating the state variables with the purpose of using them in the sensors fault diagnosis, using the analytical redundancy concept, likewise, from the estimation of these variables other non-measurable can be determined; for example, the caloric power. The stability analysis and observers gains are obtained by linear matrix inequalities (LMIs). The observers are validated by MATLAB® simulations to verify the observers convergence and analyze their response under system disturbances.

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“…Therefore, it is efficiently to use buck-converter in order to receive certain value. Analogic researches were made in [1] for boost-voltage converter with digital system control and in [2] for buck-boost converter with voltage feedback control. Therefore, the objects of research are processes in electrical scheme and enormous digital control systems in converter.…”

Section: Introductionmentioning

confidence: 99%

Circuit design of buck-voltage converter with digital control system

Zheliazkov

2020

TAPR

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Modeling and Simulation of Buck-Boost Converter with Voltage Feedback Control

Cited by 15 publications

References 5 publications

Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

Analysis and Comparison of Fuzzy Models and Observers for DC-DC Converters Applied to a Distillation Column Heating Actuator

Circuit design of buck-voltage converter with digital control system

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