CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Adaptive neural network tracking control of robot manipulators with prescribed performance
Xiaoliang Xie; Long Cheng; Zeng-Guang Hou; Cheng Ji; Min Tan; Hongnian Yu
2010
Conference NameWorkshop on Human Adaptive Mechatronics
Conference Date MAY, 2010
Conference PlaceLoughborough
CountryEngland
AbstractIn this paper, a controller for robot manipulators is proposed to guarantee the tracking error of the systems bounded by predefined decreasing boundary. In this control scheme, a multi-layer neural network is used to approximate the unknown non-linear items, and the robustifying control term is used to compensate the approximation errors. The adaptive laws of weights of the neural network and robustifying control term are derived based on the Lyapunov stability analysis, so that, under appropriate assumptions, the transient and steady-state error bounds can be guaranteed. Compared with the existing work, the adaptable parameters in the proposed method do not need an off-line training procedure for better approximation. Simulations performed on a two-link robot manipulator illustrate the developed controller and demonstrate its performance.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23153
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Xiaoliang Xie,Long Cheng,Zeng-Guang Hou,et al. Adaptive neural network tracking control of robot manipulators with prescribed performance[C],2010.
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