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 .
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 ∞ 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 ∞ 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 ∞ controller. As the result, L 2 gain can be attenuated by the presented fuzzy neural network structure and adaptive algorithm.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2002年第3期340-344,共5页
Control Theory & Applications
基金
supportedbyTsinghuaUniversity 985AnthropomorphicRobotProject.
