基于自适应平方根 CKF 的多传感器混合融合算法(英文)

In the normal operation condition, a conventional square-root cubature Kalman filter (SRCKF) gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.     

基于海况分级的船舶动力定位切换控制

为了使动力定位船舶可以在变化海况下持续稳定运行,本文建立了基于切换规则的多控制器系统对动力定位船舶进行控制。为了保证各控制器之间可以平滑切换,提出了一种基于积分特性的滞后切换规则。根据切换规则来选择与实际系统最接近的控制器作为当前控制器。文中证明了该切换规则可以保证整个系统的稳定性。通过仿真验证了该方法的有效性。