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Adaptive fuzzy-neural-network control for maglev transportation system
1Department of Electrical Engineering, Yuan Ze University, Chung Li, Taiwan, ROC. rjwai@saturn.yzu.edu.tw
IEEE Transactions on Neural Networks
|February 14, 2008
Summary
This study introduces an adaptive fuzzy-neural-network control (AFNNC) for magnetic-levitation (maglev) systems. AFNNC effectively manages nonlinearities and uncertainties, outperforming sliding-mode control (SMC) strategies.
Area of Science:
- Control Systems Engineering
- Robotics and Automation
- Transportation Engineering
Background:
- Magnetic-levitation (maglev) systems present significant control challenges due to inherent nonlinear and unstable dynamics.
- Existing control strategies like sliding-mode control (SMC) can suffer from chattering and limitations with uncertainty bounds.
Purpose of the Study:
- To develop and evaluate advanced control schemes for maglev transportation systems.
- To address the chattering phenomenon and improve stability in the presence of system uncertainties.
Main Methods:
- Development of a dynamic model for the maglev system, incorporating levitation and propulsion.
- Implementation of a model-based sliding-mode control (SMC) strategy.
- Introduction of an adaptive sliding-mode control (ASMC) scheme with bound estimation.
- Design of a model-free adaptive fuzzy-neural-network control (AFNNC) scheme with online learning algorithms.
Main Results:
- Numerical simulations verified the effectiveness of the proposed control schemes.
- The adaptive fuzzy-neural-network control (AFNNC) demonstrated superior performance compared to SMC and ASMC.
- AFNNC effectively mitigated chattering and ensured system stability without auxiliary controllers.
Conclusions:
- The proposed adaptive fuzzy-neural-network control (AFNNC) offers a robust and effective solution for controlling maglev transportation systems.
- AFNNC simplifies control transformations and relaxes constraints compared to conventional model-based methods.
- This research highlights the potential of AFNNC for advanced maglev control applications.
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