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🚀 Designing a Model Predictive Controller for a Buck Converter in MATLAB/Simulink

🔧 Step 1: Buck Converter Design Parameters

Before building the simulation model, we first need to define and calculate several key parameters for the buck converter:

  • Input voltage

  • Output voltage

  • Switching frequency

  • Inductor current ripple

  • Capacitor voltage ripple

  • Load resistance

Using standard equations:

  • Inductance (L) is calculated based on ripple current and switching specs.

  • Capacitance (C) is calculated based on output voltage ripple.

  • Load resistance (R) and duty cycle (D) are also derived from the voltage levels.

These values are used to set up the base hardware model in Simulink.

🛠️ Step 2: Simulink Model Setup

Once the values are calculated, the Simulink model is constructed using:

  • DC Voltage Source

  • IGBT Switch and Diode

  • Inductor (L), Capacitor (C), and Load Resistor (R)

This model represents the physical buck converter where the IGBT is controlled using a pulse-width modulated signal influenced by the duty cycle.

🧪 Step 3: Collecting System Data for MPC

To design the MPC, we need to create a linear model (transfer function) of the buck converter. This involves:

  • Applying the calculated duty cycle as input

  • Measuring the output voltage as output

  • Storing this input-output data using To Workspace blocks

🧮 Step 4: System Identification

The input-output data is used within MATLAB's System Identification Toolbox:

  1. Load time-domain data for both input (duty cycle) and output (voltage).

  2. Define simulation start time and sample time.

  3. Use the toolbox to estimate the transfer function that models the plant dynamics.

This transfer function is then exported for use in MPC design.

📐 Step 5: MPC Block Design and Integration

Using the transfer function:

  • Open a new Simulink model and search for the MPC Controller block.

  • Configure the Linear Time Invariant (LTI) System using numerator and denominator of the transfer function.

  • Connect the MPC block to the plant model.

  • Define:

    • Reference command (e.g., 50V)

    • Disturbance input (set to 0 in this example)

This setup allows the MPC block to predict the system behavior and adjust the control signal accordingly.

📊 Step 6: Simulation and Validation

With everything in place:

  • Simulate the system with a 70V input and verify that the output voltage settles at 50V, as expected.

  • Perform a step change in the reference voltage (from 50V to 40V at 0.5 seconds) to test dynamic performance.

  • The MPC successfully adjusts the duty cycle to bring the output voltage to the new reference of 40V.

The results show that the MPC tracks the reference command accurately, even with changes in desired output.

✅ Conclusion

This project demonstrates how to design a Model Predictive Controller for a buck converter using MATLAB and Simulink. From hardware modeling to data-driven system identification and real-time controller design, this workflow ensures that the buck converter responds effectively to both steady-state and dynamic voltage requirements.

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