This project demonstrates the dynamic modeling, simulation, and control of a four-bar mechanism driven by a brushless servo motor, developed as part of the MCT-241: Modelling & Simulation course.
The system was designed to analyze nonlinear dynamics, implement a state-space model, and evaluate mechanism behavior under different operating conditions. Both linearized and nonlinear models were developed, providing a comparative study of accuracy vs. computational efficiency.
The main objective was to achieve precise motion control of the mechanism while accounting for realistic motor and linkage dynamics.
- Derived mathematical models for the four-bar mechanism and brushless servo motor.
- Considered link lengths, moments of inertia, and motor constants.
- Built nonlinear state-space equations to preserve system accuracy.
- Implemented both linear and nonlinear models in MATLAB & Simulink.
- Simulated angular positions, velocities, motor torque, and current.
- Compared the linear model (simpler, faster) with the nonlinear model (more accurate).
- Designed and tested control strategies for tracking and stability.
- Evaluated controller performance under nonlinear system effects.
- 🤖 Robotics
- 🚗 Automotive Systems
- 🏭 Industrial Machinery & Automation
├──📂 MatLab_Code # MATLAB implementation (linear & nonlinear models)
├──📂 Presentation # Project presentation slides
├──📄 CEP_MS_2022_MC_45.pdf # Full project report
├──📄 CEP_MS_2022_MC_45_Simulation_Results.pdf # Simulation results
├──📄 README.md # Project README
📌 Key Insights
- Nonlinear modeling captures realistic effects like torque oscillations and nonlinear inertia.
- Linear models are faster to simulate but lose accuracy in complex or high-speed scenarios.
- Demonstrates the trade-off between computational simplicity and modeling fidelity.
- Provides a foundation for advanced control strategies in servo-driven mechanisms.
This project successfully demonstrated the dynamic modeling, simulation, and control of a servo motor-driven four-bar mechanism.
- The nonlinear model provided high-fidelity representation of system dynamics.
- The linear model worked as a simplified baseline but was less accurate in nonlinear conditions.
- Results emphasized the importance of nonlinear modeling for precision control and performance optimization.
- Course: MCT-241: Modelling & Simulation
- Department of Mechatronics & Control Engineering, UET Lahore