Robust feedback control for T-S stochastic fuzzy hyperbolic system

Abstract

This paper concerns the problems of Takagi-Sugeno (T-S) stochastic fuzzy hyperbolic control for nonlinear systems with or without parameter uncertainties. The state feedback controller is designed by parallel distributed compensation (PDC) method, and some sufficient conditions are formulated via linear matrix inequalities (LMIs) such that the system is asymptotically stable. The above approach is also extended to the output feedback controller of T-S fuzzy hyperbolic system with parameter uncertainties, and the desired controller can be easily obtained by using the Matlab LMI toolbox. The main advantage of the proposed model is that it can achieve small control amplitude via "soft" constraint approach. Finally, two simulation examples are utilized to demonstrate the validity and effectiveness of the proposed approaches.

A Spectral-timing Study of the Inner Flow Geometry in MAXI J1535-571 with Insight-HXMT and NICER

“…Insight-HXMT (Zhang et al 2014) consists of three groups of instruments: the high-energy X-ray telescope (HE; 20-250 keV, 5100 cm 2 ), the medium-energy X-ray telescope (ME; 5-30 keV, 952 cm 2 ), and the low-energy X-ray telescope (LE; 1-15 keV, 384 cm 2 ). HE contains 18 cylindrical Na I(Tl)/Cs I(Na) phoswich detectors; ME is composed of 1728 Si-PIN detectors; and LE uses a swept charge device (SCD).…”

Quantized guaranteed cost dynamic output feedback control for uncertain nonlinear networked systems with external disturbance

“…For solving the problem, Takagi-Sugeno (T-S) fuzzy model approach demonstrates extremely attractive advantage because it applies a series of local linearity to represent the whole nonlinear system, then the theories of linear system can be used directly (Liu et al, 2019;Tang et al, 2021;Wang et al, 2022). The stabilization problem for nonlinear NCSs which considered quantization and delay simultaneously was studied by using the Lyapunov functional method by Zhang et al (2010). The sampled-data control problem for nonlinear T-S fuzzy model with quantization was addressed by Han et al (2018), who designed the sampled-data controller by applying dynamic quantizer as well as the input delay method.…”

“…The nonfragile H ' filtering issue for nonlinear switched systems was presented by Zheng et al (2020), who simultaneously considered the filter parameter disturbance and the static data quantization in the T-S fuzzy model. It is worth noting that all of Zhang et al (2010), Han et al (2018), Li et al (2020a). and Zhang et al (2011) adopted dynamic quantization strategy to quantize the signals, but none of them took the external disturbances which are unavoidable in practical situations into consideration.…”