Design of an event-triggered joint adaptive high-Gain observer for a class of nonlinear system with unknown states and parameters

Zhuang, Xincheng; Wang, Haoping; Ali, Sofiane Ahmed; Tian, Yang; Song, Chengcheng

doi:10.1016/j.jfranklin.2023.03.005

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…The NLNS (1) has broad applicability in modeling of various nonlinear systems, such as Exoskeleton robots, 41 automatic ground vehicles, 42 and quadrotor UAVs. 43 The NLNS (1) encompasses the modeling of integral chain systems commonly found in these applications. This feature greatly facilitates the design of high-gain observers, 23 widely used in nonlinear systems, as well as extended state observers to handle unknown dynamics.…”

Section: Problem Formulationmentioning

confidence: 99%

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Predictor‐based continuous‐discrete nonlinear observer design subject to aperiodic sampled and delayed measurement

Zhuang,

Wang,

Tian

2024

Intl J Robust & Nonlinear

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In this paper, a predictor‐based continuous‐discrete nonlinear observer is proposed for a class of nonaffine Lipschitz nonlinear systems with aperiodic sampled delayed output measurements and external disturbance. Firstly, this study introduces a class of delayed sampling hybrid nonlinear systems. By employing Lyapunov techniques and trajectory‐based stability theory, sufficient conditions are established for ensuring input‐to‐state stability of the delayed sampling hybrid nonlinear system with respect to external disturbances. Then the predictor‐based continuous‐discrete nonlinear observer is designed which consists of a continuous‐time observer, a compensating injector and a predictor. The continuous‐time observer is utilized to obtain continuous and delay‐free state estimation. The compensating injector is designed to compensate for output errors that occur between sampling instants. Furthermore, the predictor is employed to obtain delay‐free output error information, which is then utilized by the continuous‐time observer for feedback correction. The proposed observer's exponential input‐to‐state property against the disturbance is proved via the proposed hybrid system stability theory. The effectiveness of the proposed observers has been demonstrated through numerical simulations and performance comparisons with the zero‐order holder‐based observer and the output predictor‐based observer designs in order to highlight the effectiveness and advantages of the proposed observer.

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Section: Problem Formulationmentioning

confidence: 99%

“…Remark The NLNS (1) has broad applicability in modeling of various nonlinear systems, such as Exoskeleton robots, 41 automatic ground vehicles, 42 and quadrotor UAVs 43 . The NLNS (1) encompasses the modeling of integral chain systems commonly found in these applications.…”

Section: Problem Formulationmentioning

confidence: 99%

Predictor‐based continuous‐discrete nonlinear observer design subject to aperiodic sampled and delayed measurement

Zhuang,

Wang,

Tian

2024

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method is characterized by its simplicity since only one equation of the observer is initialized, in addition to the fact that the design does not change the original state observer structure. Scholars have extended this design to different cases, such as involving unknown parameters, 22,23 Multirate Sampled‐Data Observer 24 and so forth. In Reference 25, the authors introduced a closed‐loop correction term in the output predictor structure featuring larger sampling intervals in comparison with previous design approach 21 …”

Section: Introductionmentioning

confidence: 99%

Continuous‐discrete high gain observer design with PI output predictor

Zhuang

Tian

Wang

et al. 2023

Adaptive Control & Signal

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SummaryIn this article, a continuous‐discrete high gain observer with a proportional integral (PI) output predictor structure is designed for a class of nonlinear systems subject to sampled‐delayed data measurements. To counteract the effect of the well‐known sensitivity of the high gain observer to the noise measurements, a novel PI output predictor structure is proposed. The uniform ultimate boundedness (UUB) of the proposed observer is demonstrated by means of Lyapunov function and small gain method. The effectiveness of our proposed observer is demonstrated through numerical simulations and performances comparison with previous observer design methods in the literature.

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Dynamic event‐triggered fault identification for nonlinear systems via deterministic learning

Zeng,

Chen,

Chen

et al. 2024

Asian Journal of Control

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In this paper, a fault identification strategy for nonlinear systems is proposed by combining the deterministic learning (DL)‐based adaptive high‐gain observer (AHGO) with a dynamic event‐triggered mechanism (DETM). The DL theory is utilized to satisfy the partial persistent excitation condition, while the AHGO is employed to estimate the system states and fault functions simultaneously. Two DETMs are adopted to reduce data transmission and computational burden. The inter‐event intervals of the considered event‐triggered mechanisms are proven to be positive, thus excluding the Zeno phenomenon. The novelty of this paper lies in that, through the special design of AHGO and event‐triggered conditions, the estimation errors can converge to zero with arbitrary precision. Meanwhile, by incorporating the estimated output error into the DETM design, it is demonstrated that the number of events can be adaptively adjusted based on the fault signal. Furthermore, the relationship between the observer gain and system performance, as well as the inter‐event interval, is revealed (The event‐triggered mechanisms design method that ensures exponential convergence of the observer). Finally, the effectiveness of the developed strategy is verified through a simulation example.

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Design of an event-triggered joint adaptive high-Gain observer for a class of nonlinear system with unknown states and parameters

Cited by 10 publications

References 42 publications

Predictor‐based continuous‐discrete nonlinear observer design subject to aperiodic sampled and delayed measurement

Predictor‐based continuous‐discrete nonlinear observer design subject to aperiodic sampled and delayed measurement

Continuous‐discrete high gain observer design with PI output predictor

Dynamic event‐triggered fault identification for nonlinear systems via deterministic learning

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