Estimation of the state and parameter of full electric driven ground unmanned platform with variable configuration

Abstract

A ground unmanned platform with variable configuration can better adapt to the field combat environment, which can adjust the wheelbase and wheel track to adapt to the ground. The power system of the platform is distributed drive form, with better controllability, higher drive efficiency, and faster system response. Changes of wheelbase and wheel track can influence the position of the platform’s centroid, which is critical for dynamic control and platform state acquisition. Based on the 9DOF nonlinear vehicle model, this paper introduces the relative position parameters of the vehicle centroid and the inertial measurement unit. And this paper performs coordinate transformation to accurately estimate the state parameters such as the position of the center of mass, the speed of the vehicle, and the centroid side-slip angle according to the measurement data, avoiding estimation error due to the change of centroid position. On the basis of fusing on-board multi-sensor information such as wheel hub torque, a square-root Unscented Kalman Filtering algorithm (SR-UKF) with high stability performance that can adapt to strong nonlinear system is proposed. In the Simulink/Trucksim joint platform, the double lane change experiments with different intermediate axis positions show that the estimation method can observe the driving status and parameters of the vehicle in real time, and the tracking effect is better.