A Recurrent Neural Multi-Model for Mechanical Systems Dynamics Compensation

被引：0

作者：

Baruch, Ieroham ^{[1
]}

Beltran, Rafael ^{[1
]}

Garrido, Ruben ^{[1
]}

Nenkova, Boyka ^{[2
]}

机构：

[1] CINVESTAV IPN, Dept Automat Control, Mexico City 07360, DF, Mexico

[2] Inst Informat Technol, Sofia 1113, Bulgaria

来源：

CYBERNETICS AND INFORMATION TECHNOLOGIES | 2005年 / 5卷 / 02期

关键词：

Recurrent neural networks; back propagation learning; fuzzy-neural multimodel; systems identification; adaptive control; mechanical system with backlash;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with backlash. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. Simulation results confirm the applicability of the proposed intelligent control system, where a good convergence of all recurrent neural networks, is obtained.

引用

页码：21 / 31

页数：11

共 25 条

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[2]

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[3]

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[8]

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[9]

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[10]

Baruch IS, 2000, LECT NOTES ARTIF INT, V1904, P292

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