| Expert S-surface control for autonomous underwater vehicles |
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| 作者姓名： | 张磊 庞永杰 苏玉民 赵福龙 秦再白 |
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| 作者单位： | State Key Laboratory of Autonomous Underwater Vehicle，Harbin Engineering University |
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| 基金项目： | Supported by the National Natural Science Foundation of China under Grant No.50579007. |
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| 摘 要： | S-surface control has proven to be an effective means for motion control of underwater autonomous vehicles （AUV）. However there are still problems maintaining steady precision of course due to the constant need to adjust parameters, especially where there are disturbing currents. Thus an intelligent integral was introduced to improve precision. An expert S-surface control was developed to tune the parameters on-line, based on the expert system, it provides S-surface control according to practical experience and control knowledge. To prevent control output over-compensation, a fuzzy neural network was included to adjust the production rules to the knowledge base. Experiments were conducted on an AUV simulation platform, and the results show that the expert S-surface controller performs better than an S-surface controller in environments with currents, producing good steady precision of course in a robust way.
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| 关 键 词： | 水下交通工具 表面控制 专家控制 智能技术 |
Expert S-surface control for autonomous underwater vehicles |
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| Lei Zhang,Yong-jie Pang,Yu-min Su,Fu-long Zhao,Zai-bai Qin.Expert S-surface control for autonomous underwater vehicles[J].Journal of Marine Science and Application,2008,7(4):236-242. |
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| Authors: | Lei Zhang Yong-jie Pang Yu-min Su Fu-long Zhao Zai-bai Qin |
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| Institution: | State Key Laboratory of Autonomous Underwater Vehicle, Harbin Engineering University, Harbin 150001, China |
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| Abstract: | S-surface control has proven to be an effective means for motion control of underwater autonomous vehicles(AUV).However there are still problems maintaining steady precision of course due to the constant need to adjust parameters,especially where there are disturbing currents.Thus an intelligent integral was introduced to improve precision.An expert S-surface control was developed to tune the parameters on-line,based on the expert system,it provides S-surface control according to practical experience and control knowledge.To prevent control output over-compensation,a fuzzy neural network was included to adjust the production rules to the knowledge base.Experiments were conducted on an AUV simulation platform,and the results show that the expert S-surface controller performs better than an S-surface controller in environments with currents,producing good steady precision of course in a robust way. |
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| Keywords: | autonomous underwater vehicle S-surface control expert control intelligent integral fuzzy neural network |
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