Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties
Abstract
:1. Introduction
- A new necessary and sufficient condition for admissibility of SFOSs is presented, which contains no equality constraints.
- By expanding the dimension of the SFOS, a new sliding surface is constructed.
- Based on RBF neural network method, is constructed to estimate the nonlinear term . The restricted assumption that is norm bounded in [41] is removed.
- The adaptive control law is exploited to guarantee that the SFOS reaches the sliding surface in a finite time.
2. Preliminaries
3. Main Results
4. Simulation Example
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Zhang, X.; Huang, W. Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties. Fractal Fract. 2020, 4, 50. https://doi.org/10.3390/fractalfract4040050
Zhang X, Huang W. Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties. Fractal and Fractional. 2020; 4(4):50. https://doi.org/10.3390/fractalfract4040050
Chicago/Turabian StyleZhang, Xuefeng, and Wenkai Huang. 2020. "Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties" Fractal and Fractional 4, no. 4: 50. https://doi.org/10.3390/fractalfract4040050