A novel method for estimating the track-soil parameters based on Kalman and improved strong tracking filters. (November 2015) - Yao, Yu; Cheng, Kai; Zhou, Zhi-Jie; Zhang, Bang-Cheng; Dong, Chao; Zheng, Sen
Abstract: A tracked vehicle has been widely used in exploring unknown environments and military fields. In current methods for suiting soil conditions, soil parameters need to be given and the traction performance cannot always be satisfied on soft soil. To solve the problem, it is essential to estimate track-soil parameters in real-time. Therefore, a detailed mathematical model is proposed for the first time. Furthermore, a novel algorithm which is composed of Kalman filter (KF) and improved strong tracking filter (STF) is developed for online track-soil estimation and named as KF–ISTF. By this method, the KF is used to estimate slip parameters, and the ISTF is used to estimate motion states. Then the key soil parameters can be estimated by using a suitable soil model. The experimental results show that equipped with the estimation algorithm, the proposed model can be used to estimate the track-soil parameters, and make the traction performance satisfied with soil conditions. Highlights: A detailed mathematical model is developed which is composed of dynamics, kinematics and track-soil sub-models. A novel method based on Kalman and improved strong tracking filters (KF–ISTF) is developed for estimating the track-soil parameters online. An experimental system is designed for testing the KF–ISTF algorithm. Simulation and experimental results show that the novel method based on KF–ISTF can be used to estimate the track-soil parameters accurately.