⚡ Designing a SEPIC Converter with PID Control Using ChatGPT 🤖

lms editor
22 hours ago
2 min read

we explore the process of designing a SEPIC (Single-Ended Primary-Inductor Converter) using a closed-loop PI controller with the help of ChatGPT and MATLAB/Simulink. The step-by-step guidance shows how artificial intelligence can support power electronics design, from defining specifications to circuit simulation and controller tuning.

🔍 Step 1: Using ChatGPT for Converter Design

The design process begins by prompting ChatGPT to act as an electronics engineer and assist with creating a SEPIC converter that includes a closed-loop PI controller. The prompt includes specifications such as:

Input Voltage Range: 24–48V
Output Voltage: 48V
Output Power: 500W
Switching Frequency: 100 kHz
Output Current: Approx. 10.4 A

ChatGPT generates values for essential components such as inductors (L1, L2), capacitors (C1, C2), switch, and diode, forming the foundational elements of the SEPIC circuit.

🧱 Step 2: MATLAB/Simulink Circuit Setup

The next step is to implement the generated design in Simulink using manual block construction:

Sources and Components: DC voltage source, RLC branches, MOSFET switch, diode, inductors, and capacitors are added.
Initial Component Values:
- Inductors (L1, L2): 47 µH
- Coupling Capacitor (C1): 22 µF
- Output Capacitor (C2): 100 µF

The load resistance is calculated using the formula R=V2/PR = V^2 / PR=V2/P, ensuring proper power delivery under expected conditions.

🌀 Step 3: Open-Loop Simulation

With the basic circuit assembled, an open-loop simulation is performed using a fixed duty cycle (0.5) and a PWM generator operating at 100 kHz. The system initially gives an output voltage of ~30V, below the desired 48V, indicating that open-loop control is insufficient for achieving target performance.

🔄 Step 4: Introducing Closed-Loop PI Control

To improve system performance, a closed-loop feedback system is implemented using a PI controller:

Sum block is used to compare the reference (48V) and actual output.
The error is fed to a PI controller.
Initially used gain values: Kp = 0.52.

However, the output still fails to meet requirements, prompting further tuning.

🧪 Step 5: Tuning the PI Controller

Using ChatGPT, the designer requests optimized Kp and Ki values. Suggested values include:

Kp = 1.2, Ki = 700

After implementing these, output remains unstable due to duty cycle limits. A trial-and-error approach is adopted:

Testing different values like Kp = 0.66, Ki = 0.1
Further refined to Kp = 0.75, Ki = 480

These adjustments reduce voltage ripple and improve regulation. Results indicate that tuning significantly influences system behavior.

💡 Final Thoughts: Power of ChatGPT in Design Automation

The video demonstrates how ChatGPT can be a valuable assistant in power electronics design:

Helps generate circuit parameters
Assists in formulating Simulink models
Provides initial controller gain suggestions
Supports tuning through iterative improvement

While auto-tuning or model-based techniques could further enhance accuracy, this approach shows how combining AI tools with engineering intuition can streamline converter design processes.