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, L2 gain can be attenuated by the presented fuzzy neural network structure and adaptive algorithm.%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.
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