State-Space Analysis & Simulation of Control System: This repository contains a Python-based simulation for a control system. The project explores State-Space Modeling and compares traditional control techniques with optimal control strategies.
System Overview The system is modeled as a second-order state-space system using physical parameters (Inertia, Damping, Resistance, and Inductance).
Control Strategies Implemented: Open Loop: Baseline performance without feedback. State-Feedback (Pole Placement): Tuning the system response by manually assigning desired closed-loop poles. LQR (Linear Quadratic Regulator): An optimal control approach that minimizes a cost function to balance performance and control effort.
Performance Analysis The simulation generates a step response comparison (see Motor Speed Control: Controller Comparison plot) to evaluate:
- Rise Time: How fast the motor reaches its target.
- Settling Time: How long it takes to stabilize.
- Steady-State Error: The difference between the desired and actual speed.
How to Run Prerequisites You need Python and the following libraries: pip install numpy matplotlib control Execution Simply run the script: python control_sim(1).py
Development Note This project was developed as part of academics of Intelligent Control Systems. The simulation logic was originally prototyped in Google Colab and refined into a standalone Python script for better modularity.