✈️ Real-World Aerospace Optimization & Control Problems

Note: This repository showcases a collection of optimization and control theory projects I worked on during my Master's studies in Aerospace Engineering at KTH. These projects demonstrate practical applications of mathematical optimization and control systems in aerospace engineering.

This repository contains projects that bridge optimization and control theory for real-world aerospace problems:

• Optimize spare parts distribution & inventory using MILP/ILP
• Implement control systems for nonlinear aerospace dynamics (e.g., B747 pitch tracking, four-tank benchmark system)

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Features

🛠 Optimization (MILP/ILP)

• Multi-period planning for aerospace spare parts
• Cost minimization across transportation, purchase, holding, and shortage
• Truck vs Air transport modes with real-world constraints (capacity, lead times)
• Visual analytics for decision support

🎛 Control Systems

• Linear Quadratic Integral (LQI) Controller for pitch tracking of a Boeing 747
• Four-Tank Process: nonlinear benchmark for multivariable control
  • Nonlinear + linearized models
  • Decentralized PI control
  • Robustified control (Glover–McFarlane loop shaping)
  • Data visualization (tank levels, inputs, RGA heatmaps, sensitivity plots)

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Prerequisites

• Python 3.11+ (or PyPy3.11+, but note SciPy is not fully supported in PyPy)
• Jupyter Notebook (for running models interactively)
• Install dependencies from requirements.txt:
  • Optimization: PuLP, NumPy, Pandas, Matplotlib
  • Control: SciPy, python-control, Matplotlib

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Installation

git clone https://github.com/dhruvhaldar/Realistic-aerospace-optimization-problems.git
cd Realistic-aerospace-optimization-problems

# Create venv
python3 -m venv venv
source venv/bin/activate   # Linux/macOS
# OR
venv\Scripts\activate      # Windows

# Install dependencies
pip install -r requirements.txt