ProfessorsProfessors of Universität Duisburg-EssenFateme Bakhshande

Fateme Bakhshande

Universität Duisburg-Essen

H-index: 6

Europe-Germany

About Fateme Bakhshande

Fateme Bakhshande, With an exceptional h-index of 6 and a recent h-index of 5 (since 2020), a distinguished researcher at Universität Duisburg-Essen, specializes in the field of Nonlinear and robust control, PI-Observer, Kalman Filter, Unknown input observers, Robotics - Visual servoing.

His recent articles reflect a diverse array of research interests and contributions to the field:

New Metric for Evaluation of Deep Neural Network Applied in Vision-Based Systems

What is needed to build a precision-and task-oriented drone for agricultural use: State-of-the-art and new perspectives

Computationally efficient model predictive control for real time implementation experimentally applied on a hydraulic differential cylinder

Adaptive neural network based predictive control of nonlinear systems with slow dynamics

Robust control of a hydraulic cylinder using an observer-based sliding mode control: Theoretical development and experimental validation

Adaptive Step Size Control of Extended/Unscented Kalman Filter Using Event Handling Concept

Fateme Bakhshande Information

University	Universität Duisburg-Essen
Position	___
Citations(all)	146
Citations(since 2020)	110
Cited By	78
hIndex(all)	6
hIndex(since 2020)	5
i10Index(all)	5
i10Index(since 2020)	4
Email	Access Email
University Profile Page	Universität Duisburg-Essen
Google Scholar	View Google Scholar Profile

Fateme Bakhshande Skills & Research Interests

Nonlinear and robust control

PI-Observer

Kalman Filter

Unknown input observers

Robotics - Visual servoing

Top articles of Fateme Bakhshande

Title	Journal	Author(s)	Publication Date
New Metric for Evaluation of Deep Neural Network Applied in Vision-Based Systems	Applied Sciences	Fateme Bakhshande Daniel Adofo Ameyaw Neelu Madan Dirk Söffker	2022/3/23
What is needed to build a precision-and task-oriented drone for agricultural use: State-of-the-art and new perspectives	Agricultural Engineering AgEng2021	Fateme Bakhshande Dirk Söffker	2021
Computationally efficient model predictive control for real time implementation experimentally applied on a hydraulic differential cylinder	International Journal of Modelling, Identification and Control	Minh-Khai Nguyen Kim Anh Nguyen Thi-Thanh-Van Phan Van-Quang-Binh Ngo	2020
Adaptive neural network based predictive control of nonlinear systems with slow dynamics		Mark Spiller Fateme Bakhshande Dirk Söffker	2020/8/17
Robust control of a hydraulic cylinder using an observer-based sliding mode control: Theoretical development and experimental validation	Control Engineering Practice	Fateme Bakhshande Resida Bach Dirk Söffker	2020/2/1
Adaptive Step Size Control of Extended/Unscented Kalman Filter Using Event Handling Concept	Frontiers in Mechanical Engineering	Fateme Bakhshande Dirk Söffker	2020/1/28