Project

# Title Team Members TA Documents Sponsor
23 Boost Converter Design Agent based on PE-GPT and PANN
 Haojun Li
Jiaming Ma
Jiarong Xu
Xinyu Zhan
 design_document1.pdf
final_paper1.pdf
presentation1.pptx
presentation2.pptx
 Fanfan Lin
## **People**


Jiarong Xu, Haojun Li, Xinyu Zhan, Jiaming Ma

## **Problem**


Designing power electronics, such as boost converters, traditionally requires bridging a significant gap between high-level natural language design requirements and complex physics-based hardware realization. This design process is often time-consuming, requires extensive domain expertise, and relies on disjointed tools for calculation, simulation, and troubleshooting. Currently, the industry lacks an intelligent, automated closed-loop workflow. There is a critical need for an agent, which can autonomously plan and decompose the design workflow for users, seamlessly translating high-level requirements into verified component parameters, guiding physical assembly, and executing automated hardware diagnostics.

## **Solution Overview**


We propose a "Boost Converter Design Agent" based on PE-GPT and PANN to achieve an automated, closed-loop workflow. Central to this system is PE-GPT's ability to act as an intelligent assistant that autonomously plans and decomposes the entire complex hardware design workflow for the user. The system follows a "Brain-Planning-Tool" architecture: The LLM "Brain" (GPT-4 with RAG) handles natural language understanding. The "Planning" module uses Chain-of-Thought (CoT) reasoning to translate initial user requirements into a clear, step-by-step execution plan (spanning Planning, Design, Assembly, and Diagnosis phases), guiding the user through the process. Subsequently, the agent invokes the "Tool" module (integrating PANN) for fast forward-mode simulation (generating dynamic waveforms to validate the design) and inverse-mode diagnosis. Overall, the agent does not merely calculate optimal L and C parameters; it orchestrates the entire process, guiding users step-by-step to assemble PCB modules and analyzing experimental data to complete a closed-loop diagnosis.

## **Solution Components**


**1. AI Agent and Interactive Planning Subsystem (Software)**
* LLM Core (GPT-4) & RAG Module: Responsible for natural language understanding and retrieving specialized domain knowledge * for power electronics.
* Workflow Planner (CoT Reasoning Controller): The core of the planning process. It interacts with the user to autonomously decompose high-level design tasks into a strictly logical, step-by-step plan before execution, providing the user with a clear global view and operational steps.


**2. Simulation and Diagnostic Subsystem (Software/Algorithm)**
* PANN Forward Mode (Fast Simulation): Acts as a fast simulation tool to generate dynamic waveforms based on the agent's determined L and C parameters, validating the design prior to physical assembly.
* PANN Inverse Mode (Twin Diagnosis): Analyzes experimental data gathered from the physical circuit, diagnoses performance deviations, and provides specific adjustment suggestions.


**3. Hardware Execution and Data Acquisition Subsystem (Hardware)**
* Plug-and-Play PCB Modules: Modular inductors, capacitors, and switching components that allow users to quickly configure and assemble the physical boost converter, guided by the steps planned by PE-GPT.
* Data Acquisition Unit (Sensors/Microcontroller): Used to capture real-time experimental data (voltage/current waveforms) from the assembled boost converter and feed it back to the diagnostic system for closed-loop analysis.

## **Criterion for Success**


* Successful Workflow Planning: The PE-GPT agent must successfully interpret a natural language design request and output a clear, logical, step-by-step design workflow plan for the user.
* Successful Parameter Design: The agent must accurately calculate the optimal component parameters (Inductance L, Capacitance C) for the boost converter.
* Successful Simulation Validation: The PANN tool must successfully generate dynamic waveforms that validate the feasibility of the hardware design prior to physical assembly.
* Successful Hardware Assembly: Guided by the agent's planned steps, the user must successfully assemble the physical boost converter using the plug-and-play PCB modules and achieve the intended voltage boost function.
* Successful Closed-Loop Diagnosis: The Twin Diagnosis module must successfully ingest physical experimental data, identify discrepancies between the physical hardware and theoretical simulation, and output valid adjustment suggestions.

RFI Detector

Featured Project

Problem Statement:

Radio frequency interference from cell phones disrupts measurements at the radio observatory in Arecibo, Puerto Rico. Many visitors do not comply when asked to turn their phones off or put them in airplane mode.

Description:

We are planning to design a handheld device that will be able to detect radio frequency interference from cell phones from approximately one meter away. This will allow someone to determine if a phone has been turned off or is in airplane mode.

The device will feature an RF front end consisting of antennas, filters, and matching networks. Multiple receiver chains may be used for different bands if necessary. They will feed into a detection circuit that will determine if the power within a given band is above a certain threshold. This information will be sent to a microcontroller that will provide visual/audible user feedback.