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周 健,王晨旭,张安民. 基于双体结构的无人测绘艇航向控制[J]. 科学技术与工程, 2020, 20(16): 6510-6514.
Zhou-Jian,Wang-Chenxu,张安民.Study on Course Control of Unmanned Surveying and Mapping Vehicle Based on Catamaran Structure[J].Science Technology and Engineering,2020,20(16):6510-6514.
基于双体结构的无人测绘艇航向控制
Study on Course Control of Unmanned Surveying and Mapping Vehicle Based on Catamaran Structure
投稿时间:2019-08-03  修订日期:2020-06-14
DOI:
中文关键词:  航向控制  无人测绘艇 RBF神经网络 PID算法
英文关键词:course control unmanned surveying and mapping vehicle RBF neural network PID algorithm  
基金项目:国家重点研发计划资助(2018YFC1407400)
        
作者单位
周 健 天津大学海洋科学与技术学院
王晨旭 天津大学海洋科学与技术学院
张安民 天津大学海洋科学与技术学院
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中文摘要:
      无人艇在海洋测绘中的应用越来越广泛,而航向稳定性是实现其自主航行的重要基础。本文设计一种双体结构的无人测绘艇,用于搭载测量设备并实现测绘功能。为了实现无人艇的航向控制,将RBF神经网络与PID算法相结合,利用RBF神经网络的自学习能力实现对PID控制器参数的整定。在仿真过程中,将RBF-PID自适应模型、单一PID模型以及作为对照组引入的BP-PID自适应模型分别仿真,并在同一时刻加入随机扰动,观察系统响应效果。结果表明,基于RBF-PID算法的无人艇航向控制器的超调量为零、稳态时间最短,同时能够及时有效地纠正随机扰动的影响,保障无人测绘艇的航向稳定性。
英文摘要:
      The application of unmanned surface vehicles in ocean surveying and mapping is more and more extensive, and course stability is an important basis for its autonomous navigation. An unmanned surveying and mapping catamaran for carrying measurement equipments and achieving mapping was designed in this paper. In order to realize course control of the unmanned vehicle, the RBF neural network was combined with the PID algorithm, and the self-learning ability of the RBF neural network can adjust the parameters of the PID controller autonomously. In the simulation process, the RBF-PID adaptive model, the single PID model and the BP-PID adaptive model which was introduced as the control group were simulated separately, and random disturbances were added at a same time to observe the system response. The results show that the course controller based on RBF-PID algorithm has zero overshoot and the shortest steady-state time. At the same time, it can correct the influence of random disturbances timely and effectively to ensure the heading stability of the unmanned surveying and mapping vehicle.
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