https://github.com/ziraddingulumjanly/simulation-based-optimal-control-methods
This project explores optimal control through LQR tuning and simulation-based optimization methods. It includes applications such as control gain selection for dynamic systems and constrained resource extraction using direct methods.
https://github.com/ziraddingulumjanly/simulation-based-optimal-control-methods
Last synced: 5 months ago
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This project explores optimal control through LQR tuning and simulation-based optimization methods. It includes applications such as control gain selection for dynamic systems and constrained resource extraction using direct methods.
- Host: GitHub
- URL: https://github.com/ziraddingulumjanly/simulation-based-optimal-control-methods
- Owner: ziraddingulumjanly
- Created: 2025-04-12T10:11:15.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-04-12T10:23:18.000Z (10 months ago)
- Last Synced: 2025-06-28T12:44:36.468Z (8 months ago)
- Language: MATLAB
- Size: 739 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Simulation-Based-Optimal-Control-Methods
This project explores optimal control through LQR tuning and simulation-based optimization methods. It includes applications such as control gain selection for dynamic systems and constrained resource extraction using direct methods.
## Simulation-Based Optimal Control – SYSE 511
This repository contains MATLAB implementations and LaTeX writeups for two optimal control tasks:
1. **LQR Design** – Includes gain tuning, pole analysis, rise-time targeting, and finite-horizon Riccati integration (Parts a–d).
2. **Nonlinear Resource Extraction** – Solves a constrained optimal control problem using direct optimization with control discretization and state simulation.
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### Instructions
1. Run all MATLAB scripts in order to generate the required plots (`.pdf`).
2. Upload the provided LaTeX file and the exported plots into Overleaf.
3. Compile the LaTeX document to produce the final report.