2020
DOI: 10.1109/access.2020.3007233
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Dynamic Fractional Order Sliding Mode Control Method of Micro Gyroscope Using Double Feedback Fuzzy Neural Network

Abstract: In this paper, a dynamic fractional order sliding mode control method based on a double feedback fuzzy neural network controller is proposed to deal with the unknown parameters and upper bound of uncertainty. Firstly, the switching function of the dynamic fractional order sliding mode control is designed, which not only fixes switching function of the ordinary sliding mode control, but also increases the fractional order, so that the switching function has a higher degree of freedom. In addition, the expert ex… Show more

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Cited by 11 publications
(2 citation statements)
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“…Comparing our results with some recent published work and showing clearly how the new design features in the current work, we would mention some comparative points as follows. Unlike previous papers on modeling and control problems of MEMS capacitive plates with sinusoidal oscillations such as [7,13,14,17,19], this Fig. 7 The article presents solution for displacing MEMS capacitive plates smoothly without any overshoot in a precise micro-meter scale, and for positioning them adjacent to the pull-in point, subjected into measurement noise and external disturbance.…”
Section: Results and Comparison Studymentioning
confidence: 99%
“…Comparing our results with some recent published work and showing clearly how the new design features in the current work, we would mention some comparative points as follows. Unlike previous papers on modeling and control problems of MEMS capacitive plates with sinusoidal oscillations such as [7,13,14,17,19], this Fig. 7 The article presents solution for displacing MEMS capacitive plates smoothly without any overshoot in a precise micro-meter scale, and for positioning them adjacent to the pull-in point, subjected into measurement noise and external disturbance.…”
Section: Results and Comparison Studymentioning
confidence: 99%
“…(12)-( 15) For further evaluation, a comparison is presented with backstepping SMC (BSMC) [46], FLS based SMC (FSMC) [47] and fractional-order neural-based controller (FNC) [48]. Table 2, presents the RMSE comparison results.…”
Section: Parametersmentioning
confidence: 99%