SmartBode Tuner 🎛️

Automated Synthesis of LTI Control Systems using Evolutionary Algorithms.

SmartBode Tuner is a professional web-based CAD platform designed to automate the synthesis of Lead/Lag compensators. It utilizes Differential Evolution algorithms to optimize control parameters ($K, T, \alpha$) based on frequency domain requirements (Bode/Nyquist stability margins).

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🏗️ System Architecture

The project follows a modern Microservices-based Architecture organized in a Monorepo:

1. Frontend Dashboard (Client)

• Path: /frontend-ui
• Tech: React, TypeScript, Vite.
• Role: Provides an interactive Single Page Application (SPA) for real-time tuning.
• Key Libraries: plotly.js (Logarithmic Bode Plots), Material UI (Design System), Axios.

2. Backend Orchestrator (Gateway)

• Path: /backend-api
• Tech: Node.js, Express, MongoDB.
• Role: Acts as an API Gateway, handles user sessions, and manages project persistence.
• Function: Bridges the communication between the React Client and the Python AI Engine.

3. AI Computational Engine (Core)

• Path: /ai-engine
• Tech: Python 3.10+, FastAPI, NumPy, SciPy.
• Role: The mathematical core. It performs the heuristic optimization using the differential_evolution algorithm from SciPy and system analysis via the python-control library.

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📂 Project Structure

SmartBodeTuner/
├── ai-engine/             # Python Microservice (Optimization Logic)
├── backend-api/           # Node.js API Gateway (Data Management)
├── frontend-ui/           # React + TypeScript SPA (Visualization)
├── docs/                  # Documentation and diagrams
└── README.md              # Project entry point

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🚀 Getting Started

Prerequisites

• Node.js (v18 or higher)
• Python (v3.9 or higher)
• Git

Installation Guide

Follow these steps to run the entire stack locally.

1. Setup AI Engine (Python)

cd ai-engine
# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Mac/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Start the Microservice (Logic)
uvicorn main:app --reload --port 8000

2. Setup Backend (Node.js)

cd backend-api
npm install
# Start the Gateway
npm start
# (Runs on port 3000 by default)

3. Setup Frontend (React + TypeScript)

cd frontend-ui
npm install

# Start the Development Server
npm run dev
# (Accessible at http://localhost:5173)

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🧪 Testing Strategy

The project adopts a rigorous testing methodology suitable for critical engineering software:

• Unit Testing: Vitest + React Testing Library for component logic and isolation.
• E2E Testing: Cypress for full optimization flow validation.
• Static Analysis: TypeScript (Strict Mode) to ensure type safety for physical quantities.

✨ Key Features

• Automatic Tuning: Finds optimal $K, T, \alpha$ for Lead/Lag networks in seconds.
• Interactive Plots: Zoomable, log-scale Bode and Nyquist diagrams.
• Constraint Handling: Automatically rejects unstable closed-loop solutions.
• Project History: Save and compare different tuning versions (stored in MongoDB).

👤 Author

MATTIA FRANCHINI & MICHELE BISIGNANO