Presentation Details
Robust Adaptive Feedback Linearization Control Using Online Neural-Network Estimators for Uncertain Linear Induction Motor Drive System
ID：124
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Updated Time：2021-06-19 16:58:58 Hits：1507
Oral Presentation
Start Time：2021-07-02 16:20（Asia/Shanghai）
Duration：20min
Session：S1 Concurrent Session 1 »
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Abstract
This paper presents a robust adaptive feedback linearization control (RAFLC) using Takagi-Sugeno-Kang (TSK)-type recurrent Petri fuzzy-neural-network (T-RPFNN) for accomplishing superior dynamic performance for the linear induction motor (LIM) drive system. The RAFLC includes a FLC, a T-RPFNN estimator and an adaptive PI controller. The FLC is used to stabilize the LIM drive and the T-RPFNN estimators are utilized to approximate the nonlinear functions of the LIM and the adaptive PI controller is utilized to reduce the chattering in the control inputs. Furthermore, the Lyapunov stability analysis is employed to ensure the RAFLC approach stability. The experimental results endorse the proposed RAFLC robustness even at uncertain dynamics existence and external disturbances.
Keywords
Feedback Linearization， Neural- Network， Linear Induction Motor
Speaker
Mahmoud F. Elmorshedy
Electrical Power and Machines Engineering Department; Faculty of Engineering; Tanta UniversitySubmission Author
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Important Date
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Conference Date
Jul 01
2021
toJul 04
2021
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Jul 03 2021
Contribution Submission Deadline
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Nov 03 2021
Registration deadline
Sponsored By
Huazhong University of Science and Technology, China
Supported By
University of Sydney, Australia
Southwest Jiaotong University, China
Southwest Jiaotong University, China
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