Development of a novel nonlinear estimator based on state-dependent Riccati equation technique for articulated vehicles

In this article, for the first time, a novel nonlinear estimator based on state-dependent Riccati equation filter technique is developed for state estimation of the articulated heavy vehicles. The state-dependent Riccati equation filter approach has a structure similar to the Kalman filter, and compared to the Kalman filter, which is based on linearization, the state-dependent Riccati equation filter is based on parameterization. Also, the state-dependent Riccati equation approach due to the nonuniqueness of the state-dependent coefficient matrix prevented singularity (uncontrollability or unobservability), and this advantage can be used to improve the efficiency of the system. For this purpose, using the Newton's method, a ten-degrees-of-freedom nonlinear model of the articulated heavy vehicle including the longitudinal, lateral and yaw motion of the tractor, the articulation angle, and rotational motion of each wheel is developed. Then, the developed model of articulated heavy vehicle is verified using nonlinear TruckSim model in high-speed lane change maneuver. The vehicle model validation results indicated that the development model is near to the actual articulated vehicle nonlinear model and can be utilized in the nonlinear estimator design. Then, using the state-dependent Riccati equation filter approach, the state variable estimation algorithm based on parametrization is designed and the articulated vehicle states are estimated online. In order to assess the performance and the efficiency of the developed estimator, the simulations with two standard maneuvers including low-speed 90 ° turn maneuver and high-speed lane change maneuver in the slippery road are performed. The simulation results indicate the remarkable impacts of the developed estimator based on state-dependent Riccati equation filter technique on the state estimation of the articulated heavy vehicles.