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Model Predictive Controller Design for Buck Converter in MATLAB

Model Predictive Controller Design for Buck Converter in MATLAB

Designing a Buck Converter:

First, we initiate the design process by determining the specifications of the buck converter, including power rating, input voltage, switching frequency, and output voltage. Utilizing equations for inductance and capacitance calculation, we determine the values of the inductor, capacitor, and load resistance required for the buck converter's operation.

Obtaining the Transfer Function Model: To design the model predictive controller, we need the transfer function model of the buck converter. By collecting input-output data and utilizing MATLAB's System Identification Toolbox, we estimate the transfer function model based on the collected data. The transfer function model represents the dynamic behavior of the buck converter, enabling us to design control strategies.

Designing the Model Predictive Controller (MPC): Once we have the transfer function model, we proceed to design the model predictive controller. The MPC aims to regulate the output voltage of the buck converter based on the reference command. We configure the MPC structure, specify the sample time, and define the linear model generation for the controller.

Simulation and Analysis: After designing the MPC, we simulate the system to observe its response to different scenarios. We analyze the input and output voltages of the buck converter under various conditions, such as step changes in the reference command. Through simulation, we validate the effectiveness of the model predictive controller in regulating the output voltage of the buck converter.

Conclusion: In this simulation, we demonstrate the implementation of a model predictive controller for a buck converter in MATLAB. The MPC effectively regulates the output voltage, ensuring stable operation of the converter under dynamic conditions. This control strategy enhances the performance and reliability of power electronic systems.

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