Comparison of Nonlinear Receding-Horizon and Extended Kalman Filter Strategies for Ground Vehicles Longitudinal Slip Estimation
Friction efforts are present in almost all mechanical applications, due to contact between bodies and there are many important situations, in which they must be properly controlled. Among these, there are tire contact forces, which is focus of many studies in autonomous vehicles and control applications on vehicle systems, since the tire forces and moments are nonlinear and may be modelled as friction efforts. Any control synthesis focused to optimize its performance must be associated to state estimators, since the efforts depend on slip variables, as longitudinal slip and sideslip angle, and it is not possible to accurately measure them. So, in this paper, two state estimation algorithms are evaluated: Extended Kalman Filter (EKF) and Moving Horizon State Estimation (MHSE), which are applied to a quarter-car model for longitudinal dynamics. It is presented that, for both traction and braking phases, the MHSE is more accurate, since it takes explicitly into account the nonlinear model in the estimation process, independently of Jacobian sensitivities to discontinuities as is the case here. So, it is demonstrated that the developed estimator may be successfully associated to controllers with the objective of optimize tire performance in traction and braking control.