Adaptive Estimation of Vehicle Dynamics Through RLS and Kalman Filter Approaches

Abstract

This article presents a new methodology for estimation of vehicle's vertical forces in order to enhance road safety. Direct measurement of vertical forces requires a complex and expensive experimental setup , which is not acceptable for ordinary passenger cars. The main contribution of this article is providing a reliable estimator of vertical tire forces by using currently available low-cost sensors. The first advantage of the proposed method is that we modified the vehicle model to take into account the roll and pitch dynamics, which makes our estimator stay robust during sharp turning or at inclined road. The other advantage is that we proposed a process to identify the vehicle parameters, instead of regarding them as known constants. This could enable our estimator to stay reliable even when the parameters are wrongly configured. The parameter identification process is based on recursive least squares (RLS) algorithm. The state observers are based on Kalman filter. The estimation process is applied and compared to real experimental data obtained in real conditions. Experimental results validate and prove the feasibility of this approach.

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

“…Several works, e.g., [26][27][28][29][30][31][32], propose a basic application of the KF observer (typically an EKF) to a very complex seven DOF (Degrees of Freedom) vehicle model with a fully non-linear tyre model (Pacejka or Dugoff model). As previously mentioned, a compromise between vehicle model complexity, results, accuracy, and computational burden should be considered.…”

Estimation and prediction of vehicle dynamics states based on fusion of OpenStreetMap and vehicle dynamics models

“…The lateral load transfer ratio LT R is defined by using four wheel vertical forces as in Equation (9). The estimation method of vertical forces at each tire is introduced in our previous work [10].…”

“…The lateral skid ratio LSR represents the loss of adhesion resulting in the lateral drift. The lateral skid ratio is defined by road friction coefficient and tire forces, as in Equation (10). The estimation of lateral tire forces and slip angle is introduced in next subsection.…”

“…With this observer, we can also obtain the lateral force per axle. To obtain the lateral force at each tire, the double track model and Dugoff model are employed, as explained in our previous work [6,10].…”

“…[13] proposes a algorithm for the curve speed prediction which addresses control loss due to excessive speed in curves. [10] proposes a vehicle full-state estimation system to describe overall vehicle dynamics.…”