DESIGN, CONSTRUCTION AND STABILIZATION OF INVERTED PENDULUM ON A MOVING CART

The project aimed to design, construct, and stabilize an inverted pendulum on a moving cart, demonstrating the practical application of modern control techniques in managing non-linear and unstable dynamic systems. To achieve this, a dynamic model of the pendulum-cart system was developed for analysis and simulation, followed by the construction of a physical prototype using appropriate mechanical and electronic components. Various control strategies, including PID, LQR, and state-space feedback, were designed, implemented, and tested to ensure the pendulum remained balanced in its upright position through real-time feedback and continuous control adjustment. A systematic methodology was adopted, beginning with mathematical modeling of the system using Newtonian and Lagrangian mechanics to derive and linearize the equations of motion. The model was simulated in MATLAB/Simulink to analyze behavior and optimize control parameters. The physical setup was built with lightweight materials, equipped with DC and stepper motors for movement, and integrated with sensors like encoders, gyroscopes, and accelerometers for real-time feedback. The control algorithms were embedded into a microcontroller, allowing real-time implementation and dynamic stabilization under various disturbances and operating conditions. The results showed successful stabilization of the inverted pendulum through effective feedback control. Among the tested controllers, the PID handled small deviations well but was less robust under disturbances, while the LQR controller provided superior performance, achieving quick settling times, minimal overshoot, and high stability. The state-space controller also demonstrated strong disturbance rejection and flexibility. The hardware tests closely matched the simulation results, confirming the model’s accuracy. Overall, the project validated that advanced control methods, particularly LQR, can efficiently stabilize complex, unstable systems, offering valuable insights for applications in robotics, autonomous systems, and adaptive control environments.