| 一种新的不下机器人运动控制方法 |
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| 引用本文： | 段群杰,张学勇,等.一种新的不下机器人运动控制方法[J].佳木斯大学学报,2000,18(4):307-312. |
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| 作者姓名： | 段群杰 张学勇 |
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| 作者单位： | [1]佳木斯大学机械工程学院，黑龙江佳木斯１５４００７ [2]大庆油田甲醇厂，黑龙江大庆１６３０００ |
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| 基金项目： | 国家教育部留学回国人员科研启动基金资助项目(教留字1999383号) |
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| 摘 要： | 对神经网络和模糊理论在水下机器人运动控制中的应用进行了探讨，提出了神经网络多步预测模型的非线性广义模糊预测控制算法，用神经网络方法实现了对水下机器人这一非线性系统的在线计算、滚动优化和在线控制，采用强化学习方法来构筑模糊控制系统中的神经网络，给出了神经网络多步预测模型及相应的控制算法和操作过程，计算机仿真结果验证了本文所提方法的有效性。
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| 关 键 词： | 水下机器人 模糊控制 强化学习 神经网络 运动控制方法 |
A NEW
MOTION CONTROL METHOD OF AUTONOMOUS
UNDERWATER VEHICLE |
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| DUAN Qun-jie ,ZHANG Xue-yong ,ZHANG Ming-jun.A NEW
MOTION CONTROL METHOD OF AUTONOMOUS
UNDERWATER VEHICLE[J].Journal of Jiamusi University(Natural Science Edition),2000,18(4):307-312. |
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| Authors: | DUAN Qun-jie ZHANG Xue-yong ZHANG Ming-jun |
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| Institution: | DUAN Qun-jie 1,ZHANG Xue-yong 2,ZHANG Ming-jun 3 |
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| Abstract: | The application of neural network and fuzzy theory in motion planning and control of Underwater Vehicle (AUV) is discussed. The general nonlinear fuzzy forecast control algorithm of the multi-step forecast model of neural network is carried out. The on-line calculation, trundling optimum and on-line control of the nonlinear system of AUV are realized with neural network. Strengthened learning is adopted to construct the neural network of fuzzy control system. The multi-step forecast model of neural network and the relevant control algorithm and operation process are carried out. The computer simulation result approved that the method is valid. |
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| Keywords: | AUV fuzzy control strengthened leaning neural network |
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