Cost-Effective Estimation of Vehicle Lateral Tire-Road Forces and Sideslip Angle via Nonlinear Sampled-Data Observers: Theory and Experiments

This article proposes a cost-effective method to jointly estimate the vehicle sideslip angle and lateral tire-road forces, which are crucial to improve the stability and performance of vehicle control systems. This method only requires the information from onboard sensors, readily available on mass-production vehicles. In particular, we consider the case of sampled asynchronous measurements, i.e., the vehicle sensor signals used for observer design are transmitted at arbitrary and distinct times in a certain window bound over the vehicle networked control system. To this end, we propose a new data-sampled observer design, where the asynchronous phenomenon caused by the sampling process is explicitly taken into account via a linear parameter-varying framework. Based on an augmented Lyapunov--Krasovskii functional and specific relaxation techniques, the observer design conditions are derived to guarantee an L-2-gain performance for the discrete-continuous estimation error dynamics and a maximum allowable sampling period. The observer design is recast as a convex optimization problem, subject to linear matrix inequality (LMI) constraints, which can be efficiently resolved through conventional numerical solvers. The proposed sampled-data observer is experimentally evaluated with an autonomous vehicle under several dynamic driving scenarios, performed on a real test track.