A novel dual nonlinear observer for vehicle system roll behavior with lateral and vertical coupling

The study of vehicle coupling state estimation accuracy especially in observer-based vehicle chassis control for improving road handling and ride comfort is a challenging task for vehicle industry under various driving conditions. Due to a large amount of life safety arising from vehicle roll behavior, how to precisely acquire vehicle roll state and rapidly provide for the vehicle control system are of great concern. Simultaneously, uncertainty is unavoidable for various aspects of a vehicle system, e.g., varying sprung mass, moment of inertia and position of the center of gravity. To deal with the above issues, a novel dual observer approach, which combines adaptive Unscented Kalman Filter (AUKF) and Takagi-Sugeno (T-S), is proposed in this paper. A full-car nonlinear model is first established to describe vehicle lateral and vertical coupling roll behavior under various road excitation. Considering the variation of vehicle sprung mass in the movement process, an AUKF approach is adopted to identify the sprung mass by tuning various road classification process variances of the vehicle system in real time. Then, by combing the identification sprung mass via AUKF observer and nonlinear coupling dynamics of tire lateral force, modified T-S model-based observer is developed to estimate the vehicle coupling roll state. The stability conditions for proposed T-S observer are deduced using linear matrix inequalities (LMI). Finally, using a high-fidelity CarSim® software platform, the proposed dual observer approach is verified through a J-turn test, and simulations show that more accurate are obtained by comparing with the traditional T-S approach. The research achievements develop a reasonable algorithm to apply to the vehicle chassis control system.