具有未建模动态机械臂的自适应神经网络控制  

Adaptive neural networks controller for robotic manipulators with unmodeled dynamics

作　　者：于志刚[1] 宋申民[1] 段广仁[1] 陈兴林[1] 

机构地区：[1]哈尔滨工业大学,哈尔滨市150001

出　　处：《仪器仪表学报》2006年第z1期853-855,共3页Chinese Journal of Scientific Instrument

摘　　要：对于具有未建模动态机械臂系统,基于神经网络对非线性函数的拟合特性,用径向基神经网络来补偿机械臂系统中的未建模动态,同时提出的自适应神经网络控制器,可以保证具有未建模动态的非线性机械臂系统的渐进稳定特性。仿真结果验证了这种控制策略的有效性。Unmodeled dynamics is the unavoidable nonlinear effect that can limit control performance in robotic systems.Meanwhile,it is not available for the control.Based on universal approximation results for radial basis function neural networks(RBF-NN),it has been proposed as an alternative to RBF-NN for approximating arbitrary nonlinear functions.Adaptive RBF neural network is used to design a compensator for unmodeled dynamics in robotic system.Then asymptotically stability of the system is assured by combining nominal feedback controller and adaptive law of NN.The simulation results show the validity of the control scheme.

关 键 词：未建模动态 神经网络控制 函数拟合 

分 类 号：TH7-55[机械工程—仪器科学与技术]