Abstract:The application of agricultural machinery navigation system is becoming more and more extensive, but the path tracking accuracy and robustness of the navigation system will be significantly reduced under the working conditions of large terrain, harsh working environment and complex crop types, such as hot areas and hilly areas. Steering system is the core component of agricultural machinery navigation system. Accurate steering control is the premise of achieving high-precision path tracking. Obtaining an accurate mathematical model of the steering system can effectively reduce the design difficulty and complexity of the steering control algorithm. However, due to the unmodeled dynamics such as dynamic lag, nonlinear friction, dead zone effect and environment-related disturbance in the steering system, it was difficult to establish an accurate mathematical model of the steering system by using the mechanism analysis method. Therefore, a steering system modeling method based on Kalman filter (KF) and regularized recursive least squares ( RRLS) joint identification was proposed. The identification platform was built in Matlab environment, and the simulation comparison of RLS, KF + RLS and KF + RRLS identification schemes was realized and compared, respectively. The model with the best performance was selected as the system model identified by KF + RRLS. Subsequently, a field verification test under complex working conditions was carried out. The resulted showed that the trajectory prediction of the wheel angle response of the identified model was 95. 26% consistent with the measured data, and the key dynamic characteristics were reliably reproduced and met the real-time requirements. The model provided a reliable model basis and engineering support for the design of path tracking control algorithm of agricultural machinery navigation system in complex environment.
