Real-Time Vehicle Lateral Dynamics Estimation Using State Observer and Adaptive Filter

Accurate state estimation of a vehicle is essential for ensuring the effective operation of stability control systems, particularly in dynamic road conditions. The side-slip angle serves as a crucial parameter for vehicle handling and safety control. However, the commercially available sensors for measuring side-slip angle are often expensive, prompting the utilization of estimation methods that rely on vehicle dynamics and the available sensor measurements. This paper introduces a novel observer for side-slip angles that employs a bicycle model and directly incorporates the lateral accelerometer signal through roll angle estimation. Roll angle estimates are obtained using novel complementary filters (NCF). Complementary filter tuning parameters are adjusted automatically using the recursive least square estimation technique. The estimation performance of the mentioned algorithms is verified using standard maneuvers through CarSim®.