Decentralized Identification and Control in Real-Time of a Robot Manipulator via Recurrent Wavelet First-Order Neural Network

被引：7
|
作者：
Vazquez, Luis A. [1 ]
Jurado, Francisco [1 ]
Alanis, Alma Y. [2 ]
机构：
[1] Inst Tecnol La Laguna, Tecnol Nacl Mexico, Torreon 27000, Coah, Mexico
[2] Univ Guadalajara, CUCEI, Guadalajara 44430, Jal, Mexico
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2015年 / 2015卷
关键词：
DESIGN;
D O I：
10.1155/2015/451049
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A decentralized recurrent wavelet first-order neural network (RWFONN) structure is presented. The use of a wavelet Morlet activation function allows proposing a neural structure in continuous time of a single layer and a single neuron in order to identify online in a series-parallel configuration, using the filtered error (FE) training algorithm, the dynamics behavior of each joint for a two-degree-of-freedom (DOF) vertical robot manipulator, whose parameters such as friction and inertia are unknown. Based on the RWFONN subsystem, a decentralized neural controller is designed via backstepping approach. The performance of the decentralized wavelet neural controller is validated via real-time results.
引用
收藏
页数：12
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