对于常见的将 CMAC神经网络前馈控制器和常规反馈控制器相结合的机械手轨迹跟踪控制方案 ,它的控制性能同时受神经网络前馈控制器学习能力和反馈控制器控制精度的制约。该文提出的采用 T- S型模糊神经网络的机械手轨迹跟踪自适应控制方案充分利用了 T- S模糊模型的特点和优点 ,以一种基于简化的 T- S型的模糊神经网络作为前馈控制器 ,同时反馈控制器也采用 T- S型模糊神经网络实现。针对三自由度机械手轨迹跟踪问题的仿真实验表明 ,采用 T- S型模糊神经网络的机械手轨迹跟踪自适应控制方案是可行的和有效的 For the common robotic trajectory tracking control scheme which combines CMAC neural network feedforward controller and conventional feedback controller, its control performance is restrained by both neural network feedforward controller learning ability and feedback controller control precision. The proposed T-S fuzzy neural network robot trajectory tracking adaptive control scheme makes full use of the features and advantages of the T-S fuzzy model, based on a simplified T-S fuzzy neural network as a feedforward Controller, while feedback controller also uses T- S-type fuzzy neural network. The simulation experiments on the trajectory tracking of a three-DOF manipulator show that the robot trajectory tracking adaptive control scheme based on the T- S-type fuzzy neural network is feasible and effective