An iterative observer-based fault estimation for discrete-time T-S fuzzy systems

Ma, Yongsheng; Shen, Mouquan; Du, Haiping; Ren, Yuesheng; Bian, Guangrui

doi:10.1080/00207721.2020.1746440

Cited by 10 publications

(2 citation statements)

References 34 publications

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“…Since the 0-th observer (3) is utilized to supply the initialization for the iteration part (4), the IPI observers are divided into (3) and (4). According to [35],…”

Section: Interval Estimationmentioning

confidence: 99%

Iterative proportional-integral interval estimation of linear discrete-time systems

Shen

Park

et al. 2022

IEEE Trans. Automat. Contr.

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An iterative proportional-integral interval estimation strategy for linear discrete-time systems is investigated in this paper. A sequence iterative proportional-integral observers are built on system output and unknown disturbance iterative estimation. With the help of a structure separation technique, a sufficient condition in terms of linear matrix inequality is proposed to make the observer error system be asymptotically stable. The boundary reachability of the observer error system is analyzed via zonotope. Zonotope-based iterative algorithms with and without the output integral are built to generate estimated state intervals. Compared with the existing result, the proposed algorithms render tighter estimation intervals illustrated by a simulation study.

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“…Since the 0-th observer (3) is utilized to supply the initialization for the iteration part (4), the IPI observers are divided into (3) and (4). According to [35],…”

Section: Interval Estimationmentioning

confidence: 99%

Iterative proportional-integral interval estimation of linear discrete-time systems

Shen

Park

et al. 2022

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

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“…In a discrete system, namely, the discrete time system, the variables of all or key components of the system exist in the form of discrete signals, and the state of the system changes at discrete time points. Discrete systems need to be described by difference equations, which are widely used in social, economic, and engineering systems, such as automata, impulse control, sampling regulation, and digital control [12,[27][28][29][30]. In practical control systems, some discrete dynamical systems described by difference equations are often encountered.…”

Section: Introductionmentioning

confidence: 99%

Dissipativity Analysis for a Class of Discrete-Time Neutral Stochastic Nonlinear Systems with Time Delay

Pang

Chen

et al. 2021

Discrete Dynamics in Nature and Society

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This paper focuses on the problem of dissipativity analysis for a class of discrete-time neutral stochastic nonlinear systems (DTNSNSs) with time delay and parameter uncertainties. Different from the existing results on this topic of neutral system, a kind of discretizing the neutral system is considered. Firstly, a sufficient condition of the dissipativity, which is dependent on the solution of the Lyapunov–Krasovskii technique and linear matrix inequalities (LMIs), is established. Moreover, the state-feedback controller is designed to guarantee the dissipative performance of the closed-loop system. The effectiveness of the theoretical results is finally demonstrated by a numerical example.

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State and Fault Estimation for T–S Fuzzy Nonlinear Systems Using an Ensemble UKF

Arani

Shoorehdeli²,

Moarefianpour

et al. 2022

Circuits Syst Signal Process

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An iterative observer-based fault estimation for discrete-time T-S fuzzy systems

Cited by 10 publications

References 34 publications

Iterative proportional-integral interval estimation of linear discrete-time systems

Iterative proportional-integral interval estimation of linear discrete-time systems

Dissipativity Analysis for a Class of Discrete-Time Neutral Stochastic Nonlinear Systems with Time Delay

State and Fault Estimation for T–S Fuzzy Nonlinear Systems Using an Ensemble UKF

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