System Identification of Vessel Steering With Unstructured Uncertainties by Persistent Excitation Maneuvers
System Identification of Vessel Steering With Unstructured Uncertainties by Persistent Excitation Maneuvers
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DOI：
10.1109/joe.2015.2460871
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发表时间：
2016-07
影响因子：
4.1
通讯作者：
L. Perera;P. Oliveira;C. Guedes Soares
中科院分区：
文献类型：
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作者：
L. Perera;P. Oliveira;C. Guedes Soares
本文研究了具有非结构不确定性的船舶操纵系统辨识问题。船舶转向的初始模型通过修改的二阶Nomoto模型（即，具有随机状态参数条件的非线性船舶操纵）。然而，由于存在大量的状态和参数以及系统非线性，该模型在系统识别中引入了各种困难。因此，部分反馈线性化建议简化所提出的模型，其中系统模型的非结构化不确定性也可以分离。此外，部分反馈线性化减少了状态和参数的数量以及系统的非线性，给出了所得到的降阶状态模型。然后，对两个模型（即，全状态模型和降阶状态模型），采用扩展卡尔曼滤波器（EKF）。如结果所示，降阶模型是能够成功地识别所需的状态和参数相比，在持续激励机动船舶转向的全状态模型。因此，所提出的方法可以用于广泛的系统识别应用。
System identification of vessel steering associated with unstructured uncertainties is considered in this paper. The initial model of vessel steering is derived by a modified second-order Nomoto model (i.e., nonlinear vessel steering with stochastic state-parameter conditions). However, that model introduces various difficulties in system identification, due to the presence of a large number of states and parameters and system nonlinearities. Therefore, partial feedback linearization is proposed to simplify the proposed model, where the system-model unstructured uncertainties can also be separated. Furthermore, partial feedback linearization reduces the number of states and parameters and the system nonlinearities, given the resulting reduced-order state model. Then, the system identification approach is carried out, for both models (i.e., full state model and reduced-order state model), resorting to an extended Kalman filter (EKF). As illustrated in the results, the reduced-order model was able to successfully identify the required states and parameters when compared to the full state model in vessel steering under persistent excitation maneuvers. Therefore, the proposed approach can be used in a wide range of system identification applications.