Robust observer design for T-S fuzzy singular systems with unmeasurable premise variables and partially decoupled disturbances

Mu, Yunfei; Cai, Yuliang; Ma, Dazhong; Yan, Yuqing

doi:10.1007/s11071-023-08590-9

Cited by 3 publications

(1 citation statement)

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“…In addition, it has been known that T-S fuzzy models offer a good balance between accuracy, interpretability, and computational efficiency. Thus, owing to the advantage of T-S fuzzy models, a variety of stability analysis and control synthesis results have been reported for their extended systems such as Markovian jump fuzzy systems [22,9,21,27], T-S fuzzy fractional order systems [25,14,36,35], and T-S fuzzy singular systems (FSSs) [30,3,8,19]. Among them, the FSSs have been recognized as quite useful for describing T-S fuzzy systems with undefined behaviors and strict constraints caused by physical limitations, imprecise data, operational boundaries, and so on.…”

Section: Introductionmentioning

confidence: 99%

Admissibility and event-triggered dissipative observer-based stabilization of discrete-time T-S fuzzy singular systems via fuzzy Lyapunov functions

Nguyen,

Kim

2023

Preprint

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This paper presents a new method to design an event-triggered dissipative observer-based control for discrete-time Takagi-Sugeno fuzzy singular systems by handling admissibility, dissipativity, observer-based control, and event-triggered data problems all in one. In particular, the proposed method has the advantage of being able to realize a one-step design framework that simultaneously designs both fuzzy observer and controller gains without using any iterative search algorithms. Furthermore, the proposed method provides the flexibility to set the fuzzy event weighting matrix in conjunction with other variables and supports the performance improvement of control systems by using a fuzzy Lyapunov function. Not only that, different from other eventtriggered output-feedback control techniques, the event generation function of this paper is constructed based on actual system outputs rather than estimated states so that estimation errors are not reflected in the event-triggered mechanism. Finally, two illustrative examples are given to verify the effectiveness of the proposed method.

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