基于LuGre 摩擦模型的机械臂模糊神经网络控制

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.

Fuzzy-neural-network control for robot manipulators with LuGre friction model

The dynamic model of the two-link robot manipulator based on LuGre friction is established in order to solve the problem that the control precision of manipulators is difficult to be improved of because of unknown nonlinear friction. An adaptive fuzzy-neural-network controller(AFNNC) is designed subject to the high-coupled and nonlinear of the system and numerous parameters of the LuGre friction. Fuzzy-neural-network(FNN) compensator is used to approximate the nonlinear part including the LuGre friction. The center and the width of the basic functions of the FNN can be fixed with on-line adaptation, and the approximate error is eliminated by a sliding-mode part. The stability of the close-loop system is proved by using Lyapunov function. Simulation results show the effectiveness of the control algorithm.