期刊
NEUROCOMPUTING
卷 323, 期 -, 页码 108-116出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2018.09.072
关键词
Adaptive neural network control; Robotic manipulators; External disturbance; Time-varying output constraints
资金
- Science and Technology Planning Project of Guangdong Province, China [2015B010133002, 2017B090910011]
This paper presents an adaptive neural network control scheme for a class of uncertain robotic manipulators with external disturbance and time-varying output constraints. A system transformation technique is applied to convert a constrained system into an equivalent unconstrained one for solving the time varying output constraint problem. A model-based (MB) control scheme and an adaptive neural network (NN) control scheme, respectively, are designed by using backstepping technique. All the signals in the closed-loop system are proved to be uniformly ultimately bounded (UUB) via Lyapunov synthesis. In the adaptive control scheme, neural networks are employed to approximate the unknown closed-loop dynamics and external disturbance. A planar two degrees of freedom rigid robotic manipulator is used to be an illustrative case, where the robotic manipulator is controlled respectively by the proposed schemes and an existing robust adaptive NN control method without considering time-varying output constraints. Simulation results verify that the proposed adaptive NN controller yields better control performances in comparison to the robust adaptive NN controller without considering time-varying output constraints. (C) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据