کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7115963 | 1461167 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Design of Course-Keeping Controller for a Ship Based on Backstepping and Neural Networks
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی برق و الکترونیک
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Design of Course-Keeping Controller for a Ship Based on Backstepping and Neural Networks Design of Course-Keeping Controller for a Ship Based on Backstepping and Neural Networks](/preview/png/7115963.png)
چکیده انگلیسی
Due to the existence of uncertainties and the unknown time variant environmental disturbances for ship course nonlinear control system, the ship course adaptive neural network robust course-keeping controller is designed by combining the backstepping technique. The neural networks (NNs) are employed for the compensating of the nonlinear term of the nonlinear ship course-keeping control system. The designed adaptive laws are designed to estimate the weights of NNs and the bounds of unknown environmental disturbances. The first order commander are introduced to solve the problem of repeating differential operations in the traditional backstepping design method, which let the designed controller easier to implement in navigation practice and structure simplicity. Theoretically, it indicates that the proposed controller can track the setting course in arbitrary expected accuracy, while keeping all control signals in the ship course control closed-loop system are uniformly ultimately bounded. Finally, the training ship of Dalian Maritime University is taken for example; simulation results illustrated the effectiveness and the robustness of the proposed controller.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of e-Navigation and Maritime Economy - Volume 7, June 2017, Pages 34-41
Journal: International Journal of e-Navigation and Maritime Economy - Volume 7, June 2017, Pages 34-41
نویسندگان
Qiang Zhang, Na Jiang, Yancai Hu, Dewei Pan,