| 一种水下机器人运动的过程神经元控制 |
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| 引用本文： | 唐旭东,庞永杰,李 晔.一种水下机器人运动的过程神经元控制[J].控制理论与应用,2009,26(4):420-424. |
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| 作者姓名： | 唐旭东 庞永杰 李 晔 |
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| 作者单位： | 哈尔滨工程大学,水下机器人技术圉防科技重点实验室,黑龙江,哈尔滨,150001;哈尔滨工程大学,船舶工程学院,黑龙江,哈尔滨,150001 |
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| 基金项目： | 863基金资助项目(2008AA092301); 中国博士后科学基金资助项目(20080440838); 黑龙江省博士后资助项目; 哈尔滨工程大学 基础研究基金资助项目(HEUFT08001); 水下智能机器人技术国防科技重点实验室开放课题研究基金资助项目(2008003, 2007001). |
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| 摘 要： | 过程神经网络是传统神经网络的拓展, 增加了一个对于时间的聚合算子, 从而更好地模拟了生物神经元的信息处理机制. 这是由于水下机器人运动控制系统的输入、输出均是随时间连续变化的过程量. 结合S函数和预先规划思想, 建立水下机器人过程神经元的运动控制模型. 仿真试验证明,该新型控制模型, 对于水下机器人的运动非线性控制器具有设计简单、响应速度快、超调小、鲁棒性好等优点.
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| 关 键 词： | 水下机器人 过程神经元控制算法 S函数 预先规划 |
| 收稿时间： | 2007/10/22 0:00:00 |
| 修稿时间： | 2008/8/29 0:00:00 |
A process neural control algorithm for autonomous underwater vehicle |
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| TANG Xu-dong,PANG Yong-jie and LI Ye.A process neural control algorithm for autonomous underwater vehicle[J].Control Theory & Applications,2009,26(4):420-424. |
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| Authors: | TANG Xu-dong PANG Yong-jie and LI Ye |
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| Affiliation: | Key Laboratory of Science and Technology for Nation Defence of Autonomous Underwater Vehicle, Harbin Engineering University, Harbin Heilongjiang 150001, China; College of Shipbuilding Engineering, Harbin Engineering University, Harbin Heilongjiang 150001,;Key Laboratory of Science and Technology for Nation Defence of Autonomous Underwater Vehicle, Harbin Engineering University, Harbin Heilongjiang 150001, China; College of Shipbuilding Engineering, Harbin Engineering University, Harbin Heilongjiang 150001,;Key Laboratory of Science and Technology for Nation Defence of Autonomous Underwater Vehicle, Harbin Engineering University, Harbin Heilongjiang 150001, China; College of Shipbuilding Engineering, Harbin Engineering University, Harbin Heilongjiang 150001, |
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| Abstract: | Process neural network is extended from a traditional neural network by adding an integration operator of time to better imitate the information processing system of a biological neuron. This is because both the input and output of motion control system for automatic underwater vehicles (AUVs) is processing vectors which is related with time. Combining the S function and the pre-planning idea, we develop a motion-control model for the process neuron of an AUV. Simulation results show that the new control is useful for underwater vehicles, featuring with higher precision, simpler design, faster response, and better robustness. |
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| Keywords: | autonomous underwater vehicles process neural control algorithm S function pre-planning |
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