摘要
提出一种机器人轨迹跟踪的自适应神经滑模控制。该控制方案将神经网络的非线性映射能力与变结构控制理论相结合 ,利用 RBF网络自适应学习系统不确定性的未知上界 ,神经网络的输出用于自适应修正控制律的切换增益。这种新型控制器能保证机械手位置和速度跟踪误差渐近收敛于零。
A neural network based adaptive sliding model controller is proposed for robot manipulators. This control scheme integrates the theory of VSS and the nonlinear mapping of neural network. A key feature of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. A RBF neural network is used to adaptively learn the unknown bounds of system uncertainties, and then the output of the neural network estimator is used to adjust the switching gain. This new controller can guarantee the asymptotic convergence of the tracking error to zero.
出处
《控制与决策》
EI
CSCD
北大核心
2001年第1期79-82,共4页
Control and Decision
基金
国防科技预研基金项目! (99J16 .6 .1.BQ0 2 14)
关键词
神经网络
机器人
轨迹跟踪
滑模控制
自适应控制
neural network
robot manipulators
trajectory tracking
sliding mode control
