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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
Waheed Ur Rehman,Guiyun Jiang,Yuanxin Luo,Yongqin Wang,Wakeel Khan,Shafiq Ur Rehman,Nadeem Iqbal +6 more
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
Waheed Ur Rehman,Luo Yuanxin,Jiang Gui-yun,Wang Yongqin,Xu Yun,Muhammad Nadeem Iqbal,Mansoor Ali Zaheer,Irfan Azhar,Hassan Elahi,Yang Xiaogao +9 more
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.