Disturbance rejection of fractional-order T-S fuzzy neural networks based on quantized dynamic output feedback controller

S.A. Karthick, R. Sakthivel, Y.K. Ma, S. Mohanapriya and A. Leelamani

Applied Mathematics and Computation, 2019, vol. 361, issue C, 846-857

Abstract: The disturbance rejection approach is employed for the stabilization of fractional-order neural networks described by Takagi-Sugeno fuzzy model with dynamic output feedback controller under quantization. First, an equivalent continuous frequency distributed integral-order system is formulated for the fractional-order neural networks to estimate the system state. Specifically, the dynamic output feedback control with quantization is proposed and the measurement output is quantized by logarithmic quantizer before transmission. By employing an indirect Lyapunov approach and equivalent input disturbance (EID) technique, a set of newly established sufficient conditions with corresponding quantizer’s dynamic parameters is obtained in the shape of LMIs to ensure the asymptotical stability of the considered fractional-order system. Finally, the validity of the considered design method is illustrated through a numerical example.

Keywords: Fractional-order neural networks; Dynamic output feedback control; Equivalent-input-disturbance; Quantization (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:361:y:2019:i:c:p:846-857

DOI: 10.1016/j.amc.2019.06.029

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