Modular estimation of lateral vehicle dynamics and application in optimal AFS control

The paper introduces a novel modular estimation approach for lateral vehicle and tire dynamics using a simplified vehicle model and a non-linear estimation algorithm. A dynamics-oriented representation of lateral tire forces with a single track lateral vehicle model (STVM) has been introduced. Subsequently, extended Kalman filter (EKF) based distributed observer modules for each dynamical parameter has been designed and combined into a Unified Estimation Scheme (UES). Finally, a linear quadratic regulator (LQR) based Active Front Steering (AFS) control system has been designed using the estimated parameters. The accuracy and computational efficiency of the designed scheme has been analyzed and compared to non-modular UKF, EKF, and Particle Filter (PF) algorithms, through Monte-Carlo Simulations using the CarSim dataset for both high and low mu surfaces, followed by further validation using real-time dataset. The results show that the proposed system significantly improve the accuracy and speed of estimation, as well as stable performance in closed loop control.

Automated summary

The paper presents a novel modular estimation approach for lateral vehicle and tire dynamics using a simplified vehicle model and a non-linear estimation algorithm. A dynamics-oriented representation of lateral tire forces with a single track lateral vehicle model (STVM) is introduced, along with distributed observer modules for each dynamical parameter and a Unified Estimation Scheme (UES). The designed linear quadratic regulator (LQR) based Active Front Steering (AFS) control system shows improved accuracy and computational efficiency, with stable performance in closed loop control demonstrated through simulations and real-time validation.