This project focuses on the parameter estimation of mechanical systems modelled using Lagrangian mechanics. The project involves deriving equations of motion through energy-based modeling and estimating unknown system parameters such as mass, damping, and stiffness by comparing theoretical models with experimental or simulated data. The study aims to improve the accuracy of dynamic system modeling and analysis. The project also explores computational techniques using MATLAB or Python to implement optimization and estimation algorithms. By applying numerical methods such as least squares estimation, the project demonstrates how Lagrangian modeling can be used in system identification and control applications. The work provides practical knowledge in analytical mechanics, simulation, and engineering computation.

Literature review on Lagrangian mechanics and parameter estimation techniques Derivation of equations of motion using the Lagrangian approach Development of simulation models in MATLAB or Python Implementation of parameter estimation algorithms Validation of estimated parameters using simulated or experimental data Performance analysis and comparison of results Presentation and demonstration of the developed system or simulation