Adaptive estimation of road gradient and vehicle parameters for vehicular systems

To improve the vehicle driving performance, adaptive parameter estimation is studied to simultaneously estimate the road gradient, vehicle mass and other vehicle parameters, which requires the vehicle longitudinal velocity and driving force only. Different to conventional gradient and recursive least square methods, the parameter estimation error is obtained to drive the adaptive laws for estimating unknown parameters, where exponential convergence can be guaranteed under the classical persistently excitation condition. Moreover, finite-time parameter estimation is achieved by incorporating the sliding mode technique into the adaptive laws. The robustness of the proposed adaptations against bounded disturbances is studied. Simulation results illustrate that the presented methods can obtain faster transient and better steady-state performance than some available methods.