摘要
为了解决扩展卡尔曼滤波(extended Kalman filter,EKF)算法在船舶操纵运动模型参数辨识中存在辨识精度低、稳定性差和泛化能力弱的问题,提出了一种基于均方根容积卡尔曼滤波(square root cubature Kalman filter,SRCKF)的辨识算法。在CKF框架下将方差矩阵的均方根代替原始方差矩阵,使用三角分解对其进行预测和更新以提高辨识的稳定性。将EKF作为对比算法,利用四阶龙格库塔法解算的数值仿真数据,对舵角符合舵机伺服机构变化的船舶二阶非线性响应模型参数进行辨识,并将得到的辨识模型开展泛化能力验证试验。结果表明:SRCKF算法具有比EKF算法更高的辨识精度、稳定性和泛化能力。
The system identification algorithm,based on the square root cubature Kalman filter(SRCKF)is proposed to address issues such as low accuracy,poor robustness,and weak generalization ability encountered by the extended Kalman filter(EKF)algorithm in parameters identification of ship maneuvering motion models.This algorithm,within the framework of CKF,replaces the original covariance matrix with its root mean square and utilizes triangular decomposition for prediction and update to enhance identification stability.The EKF is used as a comparison algorithm to identify the parameters of the second-order nonlinear response model of a ship with rudder angles that comply with changes in the rudder servo system using the numerical simulation data solved by the fourth-order Lunger Kuta method,and the obtained identification model is subjected to a verification test of the generalisation ability.The results indicate that the SRCKF algorithm has higher identification accuracy,stability,and generalization ability than the EKF algorithm.
作者
李晴昊
任俊生
华焱
Li Qinghao;Ren Junsheng;Hua Yan(Nautical Dynamic Simulation and Control Key Laboratory,Dalian Maritime University,Dalian 116026,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第8期1790-1799,共10页
Journal of System Simulation
基金
国家自然科学基金(51779029,61976033,51939001)
国家重点研发计划(2022YFB4301402)。
