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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 815 - 820 (6page)
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In this article, for a series of nonlinear dynamic systems, a novel neuroadaptive controller which base on dualloop recursive fuzzy neural network (DRFNN) is designed. Different from the traditional fuzzy neural network (FNN) in which parameters need to be preset and tested several times, owing to the existence of adaptive mechanism, the center vector and the base width in DRFNN do not need to be preset and debugged again and again, but are set randomly and they will eventually stabilize at the optimal values. In addition, thanks to the recursive structure, the DRFNN can store more useful information and process time varying signals effectively, consequently, the DRFNN possesses more accurate
approximation ability and stronger robustness than fuzzy neural network. Then DRFNN and global sliding mode control are combined to design the controller, and the stability of the closed-loop system is proved strictly according to Lyapunov stability criterion. Furthermore, in order to verify the practicability and effectiveness of the proposed scheme, the APF is selected as the research object for simulation. The results demonstrate that the developed control scheme has superior performance. In the end, some comparisons among RFNN and the proposed DRFNN are conducted to suggest that the DRFNN possesses more superior properties.
approximation ability and stronger robustness than fuzzy neural network. Then DRFNN and global sliding mode control are combined to design the controller, and the stability of the closed-loop system is proved strictly according to Lyapunov stability criterion. Furthermore, in order to verify the practicability and effectiveness of the proposed scheme, the APF is selected as the research object for simulation. The results demonstrate that the developed control scheme has superior performance. In the end, some comparisons among RFNN and the proposed DRFNN are conducted to suggest that the DRFNN possesses more superior properties.
목차
Abstract
1. INTRODUCTION
2. DUAL-LOOP RECURSIVE FUZZY NEURAL NETWORK (DRFNN)
3. PROPOSED CONTROLLER
4. SIMULATION STUDY FOR ACTIVE POWER FILTER
5. CONCLUSION
REFERENCES
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