This paper presents a method for identification of the hydrodynamic coefficients of the dive plane of an autonomous underwater vehicle. The proposed identification method uses the governing equations of motion to esti...This paper presents a method for identification of the hydrodynamic coefficients of the dive plane of an autonomous underwater vehicle. The proposed identification method uses the governing equations of motion to estimate the coefficients of the linear damping, added mass and inertia, cross flow drag and control. Parts of data required by the proposed identification method are not measured by the onboard instruments. Hence, an optimal fusion algorithm is devised which estimates the required data accurately with a high sampling rate. To excite the dive plane dynamics and obtain the required measurements, diving maneuvers should be performed. Hence, a reliable controller with satisfactory performance and stability is needed. A cascaded controller is designed based on the coefficients obtained using a semi-empirical method and its robustness to the uncertainties is verified by the μ-analysis method. The performance and accuracy of the identification and fusion algorithms are investigated through 6-DOF numerical simulations of a realistic autonomous underwater vehicle.展开更多
文摘This paper presents a method for identification of the hydrodynamic coefficients of the dive plane of an autonomous underwater vehicle. The proposed identification method uses the governing equations of motion to estimate the coefficients of the linear damping, added mass and inertia, cross flow drag and control. Parts of data required by the proposed identification method are not measured by the onboard instruments. Hence, an optimal fusion algorithm is devised which estimates the required data accurately with a high sampling rate. To excite the dive plane dynamics and obtain the required measurements, diving maneuvers should be performed. Hence, a reliable controller with satisfactory performance and stability is needed. A cascaded controller is designed based on the coefficients obtained using a semi-empirical method and its robustness to the uncertainties is verified by the μ-analysis method. The performance and accuracy of the identification and fusion algorithms are investigated through 6-DOF numerical simulations of a realistic autonomous underwater vehicle.
文摘采用自主水下航行器(Autonomous Underwater Vehicle,AUV)磁测平台可开展海洋地磁场测量、水下磁性目标探测和识别等工作,AUV磁测平台具有广阔的应用前景,但目前AUV载体磁干扰补偿技术研究尚不成熟,制约着水下航行器测磁精度。基于磁测平台抗磁干扰基本原理,提出一种基于线性种群规模缩减和成功历史的参数自适应差分进化(Success History-based Adaptive Differential Evolution with Linear Population Size Reduction,L-SHADE)算法的AUV载体磁干扰参数辨识的数值模拟方法。用磁偶极子和旋转椭球壳混合模型来等效模拟AUV载体磁干扰,通过模拟航行获得多组磁测数据,据此建立磁干扰参数辨识模型,并采用L-SHADE算法求解。通过数值模拟实验定量分析研究磁测平台测磁精度随磁传感器、平台姿态及航向等误差的传播规律。研究结果表明:当磁传感器测量精度为10 nT、姿态测量精度为0.01°、航向测量精度为0.1°时,测磁误差可小于100 nT。设计的AUV磁测平台抗干扰试验表明,地磁场总量最大相对误差为1.07%。
