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针对未知摩擦非线性会使机械臂控制精度难以提高的缺陷,建立基于动态LuGre摩擦的机械臂模型.在系统参数未知和机械臂负载变化的情况下,设计一种自适应模糊神经网络控制器,采用基函数中心和宽度均自适应变化的模糊神经网络补偿器,实现对系统中包括LuGre摩擦在内的非线性环节的逼近,并利用滑模控制项减小逼近误差.通过Lyapunov方法证明了闭环系统的稳定性,并通过仿真结果验证了所提出控制方法的有效性.
Aiming at the defect that the unknown friction nonlinearity makes the robot control accuracy difficult to be improved, a robot arm model based on dynamic LuGre friction is established.An adaptive fuzzy neural network controller is designed under the condition of unknown system parameters and variation of the load of the robot arm, By using the fuzzy neural network compensator whose center and width are adaptively changed, the approximation of the nonlinear link including the LuGre friction in the system is realized and the sliding mode control term is used to reduce the approximation error. The closed-loop The stability of the system is verified. The simulation results show the effectiveness of the proposed control method.