Neural and Fuzzy Adaptive Control of Induction Motor Drives

Journal Article · Thu Jun 12 04:00:00 EDT 2008 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.2952983· OSTI ID:21143556

Bensalem, Y ^[1]; Sbita, L; Abdelkrim, M N ^[2]

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1. Research Unit of Modelisation, Analyse, Command of Systems MACS (Tunisia)
2. 6029 Universite High School of Engineering-Gabes-Tunisia (Tunisia)

This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

• OSTI ID: 21143556
• Journal Information: AIP Conference Proceedings, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 1019; ISSN APCPCS; ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
ALGORITHMS
CONTROL
CONTROL SYSTEMS
FUZZY LOGIC
INDUCTION
MOTORS
NEURAL NETWORKS
NONLINEAR PROBLEMS
PERFORMANCE
TORQUE
VARIATIONS
VELOCITY