车辆系统垂向与横向耦合的侧倾状态估计

To effectively solve the problem that the coupling roll motion state of vehicle cannot be accurately obtained under complicated driving conditions and the difficulty in providing accurate input for the concurrent optimization of vehicle handling stability and ride comfort, a dual nonlinear state observer algorithm based on vehicle vertical and lateral coupling dynamics is designed to achieve real time accurate estimation of vehicle coupling roll motion state under complicated driving conditions. Firstly, the road excitation model and vehicle vertical and lateral coupling dynamics model are established. Then by utilizing the unscented Kalman filtering (UKF) technique and the nonlinear fuzzy observation (T-S) theory, a nonlinear state observation algorithm is designed and a joint-estimation on the sprung mass and rolling state of vehicle system is conducted under different road excitation conditions. Finally, by applying dynamics software CarSim^Ⓡ, the observation accuracies of vehicle roll angle and rolling rate real time estimated by joint state observer UKF&T-S on standard A- and C-grade roads are comparatively analyzed under J-turn test conditions. The results show that the UKF&T-S observer designed can effectively estimate the roll state of vehicle, with a less than 10% standard deviation of identified state, compared with the CarSim^Ⓡ simulation data.