<![CDATA[This study explores the implementation of the Kalman-Bucy observer for state estimation in a quarter-car suspension system operating under various real-world conditions. The research focuses on evaluating the observer’s performance in the presence of road surface disturbances such as speed bumps, speed humps, and potholes, combined with stochastic noise and parameter variations. To test its robustness, the system is subjected to Gaussian white noise with an intensity of 10 percent in both the process and measurement signals. Sensitivity analysis is also carried out by varying the vehicle mass between 400 kilograms in unloaded conditions and 600 kilograms when fully loaded, simulating different passenger and cargo scenarios. Simulation results demonstrate that the Kalman-Bucy observer consistently provides accurate and stable estimations of vehicle position, even in noisy and dynamically changing environments. The observer effectively filters out noise and accurately tracks the system’s dynamic response across all test scenarios. The main contributions of this research include the development of a mathematical model for a quarter-car suspension system that incorporates realistic road disturbance conditions, the formulation and implementation of the Kalman-Bucy filter for continuous-time state estimation in this system, and a thorough evaluation of the filter’s effectiveness under varying noise and disturbance conditions through MATLAB-based simulations. To further evaluate the practical value of the Kalman-Bucy observer, it is integrated into a PID control framework. The combined PID and Kalman-Bucy setup is then compared with a conventional PID controller that operates using raw measurement signals. The results indicate that incorporating the Kalman-Bucy observer significantly improves control performance by reducing oscillations, improving settling time, and enhancing the system’s ability to reject disturbances. Overall, the Kalman-Bucy observer proves to be a reliable and efficient method for state estimation and control enhancement in active suspension systems, showing strong potential for real-world automotive applications.]]>
