Neural and Fuzzy Adaptive Control of Induction Motor Drives
- Research Unit of Modelisation, Analyse, Command of Systems MACS (Tunisia)
- 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, Vol. 1019, Issue 1; Conference: CISA 08: 1. Mediterranean conference on intelligent systems and automation, Annaba (Algeria), 30 Jun - 2 Jul 2008; Other Information: DOI: 10.1063/1.2952983; (c) 2008 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
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