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Waheed Ur Rehman

Researcher at Chongqing University

Publications -  18
Citations -  165

Waheed Ur Rehman is an academic researcher from Chongqing University. The author has contributed to research in topics: Bearing (mechanical) & Control theory. The author has an hindex of 7, co-authored 18 publications receiving 115 citations. Previous affiliations of Waheed Ur Rehman include Beihang University.

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Journal ArticleDOI

Motion synchronization in a dual redundant HA/EHA system by using a hybrid integrated intelligent control design

TL;DR: In this article, an integrated fuzzy controller design approach is presented to synchronize a dissimilar redundant actuation system of a hydraulic actuator and an electro-hydrostatic actuator with system uncertainties and disturbances.
Journal ArticleDOI

Control of active lubrication for hydrostatic journal bearing by monitoring bearing clearance

TL;DR: In this paper, active hydrostatic journal bearings represent a mechatronic answer to the fast-growing industrial needs to high-performance rotary machineries, and the aim of this research is to study and improve the...
Proceedings ArticleDOI

Adaptive control for motion synchronization of HA/EHA system by using modified MIT rule

TL;DR: In this paper, a model reference adaptive control for dissimilar dual redundant actuation system that is a combination of hydraulic and electro-hydrostatic actuator is presented, where a modified form of MIT rule, called the normalized MIT rule has been given to find the controller parameters.
Proceedings ArticleDOI

Control of an oil film thickness in a hydrostatic journal bearing under different dynamic conditions

TL;DR: In this paper, a servo valve with a feedback control algorithm is presented to achieve uniform oil thickness for positioning of a shaft in a hydrostatic journal bearing. But, the proposed strategy not only has good results under the different value of viscosity but also has a linear relationship between external load and change in oil film thickness under a wide range of external load.
Proceedings ArticleDOI

The Deep Neural Network Based Classification of Fingers Pattern Using Electromyography

TL;DR: The overall results showed that designed system is able to capture optimum EMG signals having meaningful information and promises a fruitful accuracy rate of 99.3% with an average error rate of 0.7% for the given dataset.