| 引用本文: | 刘 治,李春文.基于模糊神经网络的5连杆双足机器人混杂控制（英文）[J].控制理论与应用,2002,19(3):340~344.[点击复制] |
| LIU Zhi,LI Chunwen.Five-Link Biped Robot Hybrid Control via Fuzzy Neural Networks[J].Control Theory and Technology,2002,19(3):340~344.[点击复制] |
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| 基于模糊神经网络的5连杆双足机器人混杂控制（英文） |
| Five-Link Biped Robot Hybrid Control via Fuzzy Neural Networks |
| 摘要点击 1080 全文点击 783 投稿时间：2001-03-28 修订日期：2001-12-24 |
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| DOI编号 |
| 2002,19(3):340-344 |
| 中文关键词 机器人控制 混杂控制 模糊神经网络 鲁棒控制 逆系统方法 |
| 英文关键词 robotic control hybrid control FNN robust control inverse system method |
| 基金项目 |
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| 中文摘要 |
| 针对双足机器人单脚支撑期控制问题, 提出了一种新型的模糊神经网络混杂控制方法. 该种方法结合了模糊神经网络、H∞ 控制及逆系统方法的优点. 应用了一种新的多层模糊CMAC神经网络对系统进行逼近, 一方面将模糊神经网络的构造误差看作系统的干扰, 利用H∞ 控制对干扰进行抑制. 另一方面利用模糊神经网络对系统模型进行逼近, 为逆系统的构建和H∞ 控制率的设计提供了有效的系统信息. 并证明了在采用本文提出的模糊神经网络和自适应算法后可以抑制 L2 增益. |
| 英文摘要 |
| The paper presents a new fuzzy neural networks (FNN) hybrid controller to solve the trajectory tracking problem of biped robots in the single_support phase. The advantages of fuzzy neural network, H-infinity controller and inverse system method are integrated in this paper for control purpose. A new multi-layers fuzzy CMAC is applied to approximate the system information of biped robot .On the one hand, we regard construction errors of FNN as external disturbances, and then use H-infinity controller to attenuate such disturbances. On the other hand, apply the strong approximate capability of FNN to construct the inverse system and offer efficient system information to H-infinity controller. As the result, L-2 gain can be attenuated by the presented fuzzy neural network structure and adaptive algorithm. |
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