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Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs
1Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 320, Taiwan, R.O.C.
IEEE Transactions on Neural Networks
|February 5, 2008
Summary
A novel hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) effectively controls permanent magnet linear synchronous motor (PMLSM) servo drives for precise periodic motion tracking.
Area of Science:
- Robotics and Control Systems
- Artificial Intelligence
- Electrical Engineering
Background:
- Permanent magnet linear synchronous motors (PMLSM) require advanced control for precise motion.
- Traditional control methods can struggle with the complexities of periodic reference inputs and system uncertainties.
Purpose of the Study:
- To develop a hybrid supervisory control system for PMLSM servo drives.
- To enhance tracking performance for periodic reference inputs.
- To mitigate excessive control effort and chattering.
Main Methods:
- Formulating PMLSM dynamics using field-oriented control.
- Designing a hybrid system combining supervisory and intelligent control.
- Implementing a recurrent fuzzy neural network (RFNN) as the core intelligent controller.
- Utilizing Lyapunov stability and gradient descent for RFNN online training.
Main Results:
- The hybrid system effectively stabilizes PMLSM states within predefined bounds.
- The RFNN controller smooths and reduces control effort, minimizing chattering.
- Robust tracking of various periodic reference inputs is achieved.
- The online training methodology enhances RFNN learning capabilities.
Conclusions:
- The proposed hybrid supervisory control system with RFNN offers robust and effective control for PMLSM servo drives.
- This approach significantly improves tracking accuracy and control effort management for periodic motions.
- The integration of RFNN provides adaptive learning and improved performance in dynamic environments.
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