Fuzzy observer with unknown inputs using unmeasurable premise variables for vehicle dynamics and road geometry estimation

This paper describes a methodology for estimating both vehicle dynamics and road geometry using a Fuzzy unknown input observer. Vehicle sideslip and roll parameters are estimated in presence of the road bank angle and the road curvature as unknown inputs. The unknown inputs are then estimated using the observer results. The used nonlinear model deduced from the vehicle lateral and roll dynamics with a vision system is represented by a Takagi-Sugeno fuzzy model in order to take into account the nonlinearities of the cornering forces. Taking into account the unmeasured variables, an unknown inputs TS observer is then designed on the basis of the measure of the roll rate, the steering angle and the lateral offset given by the distance between the road centerline and the vehicle axe at a look-ahead distance. Synthesis conditions of the proposed fuzzy observer are formulated in terms of Linear Matrix Inequalities (LMI) using Lyapunov method. Simulation results show good efficiency of the proposed method to estimate both vehicle dynamics and road geometry.