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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2024.10
- 수록면
- 1,017 - 1,022 (6page)
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초록· 키워드
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The study uses a recurrent meta-cognitive fuzzy neural network (RMCFNN) to present an adaptive fractional order (FO) terminal sliding mode control (TSMC) method for the robust current management of active power filter (APF). By taking into account the fact that the external disturbances and parametric perturbations of the APF are bounded, a fractional order terminal sliding mode control is created. Due to an additional degree of freedom, the suggested scheme with a FO sliding surface can provide improved finite-time high-precision tracking performance as compared to the traditional TSMC approach. Next, in order to obtain an absorbing model-free feature resulting from RMCFNN, a novel observer-based FOTSMC is constructed. The construction of specialized online updating systems for the parameters and structure of RMCFNN aims to enhance the capacity to manage uncertainties. Meanwhile, Lyapunov theory can be used to obtain finite-time convergence characteristic and closed-loop stability. Ultimately, the findings of modeling and experimentation show that the suggested observer-based FOTSMC has better control performance than other current schemes and is simple to build using a microcontroller.
목차
Abstract
1. INTRODUCTION
2. PROBLEM FORMULATION
3. RECURRENT META-COGNITIVE FUZZY NEURAL NETWORK (RMCFNN)
4. FOTSMC using RMCFNN estimator
5. Simulation Results
6. CONCLUSION
REFERENCES
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