Mutual calibration estimation of dynamic states and static parameters for skateboard chassis vehicle

The non-fixed coupling between skateboard chassis and superstructures leads to significant challenges in accurate vehicle dynamic state estimation. To address this issue, this paper proposes a mutual calibration estimation method. First, the nonlinear 7-DOF vehicle dynamic model is established as the foundation for the mutual calibration framework. Then, a novel calibrated forgetting factor recursive least squares (C-FFRLS) estimator is developed within the dynamic-static parameters mutual calibration framework, which can estimate vehicle mass and center of gravity (CoG) position while computing the yaw moment of inertia via the parallel axis theorem. The estimated static parameters calibrate both the model parameters and the noise characteristics of the dynamic observer. Finally, a calibrated adaptive robust Cubature Kalman filter (C-ARCKF) is proposed to estimate vehicle states; the dynamic observation results are used to calibrate the static observer, thereby establishing a closed-loop mutual calibration system. The simulation and experimental results validate the effectiveness of the proposed method.