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Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope
Joint Authors
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-26
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique.
Considering the system uncertainty in dynamics, neural network is employed for approximation.
Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced.
Sliding mode control is designed to cancel the effect of time-varying disturbance.
The closed-loop stability analysis is established via Lyapunov approach.
Simulation results are presented to demonstrate the effectiveness of the method.
American Psychological Association (APA)
Xu, Bin& Zhang, Pengchao. 2017. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope. Complexity،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1143163
Modern Language Association (MLA)
Xu, Bin& Zhang, Pengchao. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope. Complexity No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1143163
American Medical Association (AMA)
Xu, Bin& Zhang, Pengchao. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope. Complexity. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1143163
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143163