State estimation of the nonlinear suspension system based on nonlinear Kalman filter

In reality, a system is almost nonlinear. To estimate the parameter or state of this system, nonlinear approach is needed. The Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF) are used to estimate this nonlinear problem. EKF uses first order Taylor expansion to approximate the nonlinear system, while UKF performs a stochastic linearization by using a weighted statistical linear regression process. The purpose of this paper is to estimate the state of the nonlinear suspension system based on the Extended Kalman Filter and the Unscented Kalman Filter. The simulation deals with state estimation of nonlinear suspension system by using these filters and is compared with the true state. Also LQR controller and output feedback PD controller will be designed by aid of UKF and EKF estimation. Simulation results show that two nonlinear Kalman filters are effective in estimating the state of a nonlinear suspension system.