On observer-based controller design for Sugeno systems with unmeasurable premise variables

Moodi, Hoda; Farrokhi, Mohammad

doi:10.1016/j.isatra.2013.12.004

Cited by 57 publications

(19 citation statements)

References 36 publications

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“…where x ( k ) ∈ R n denotes the system state, u ( k ) ∈ R p 1 denotes the system input, y ( k ) ∈ R q presents the measurement output vector, f a ( k ) ∈ R p 1 denotes the additive actuator fault vector and g ( x ( k ) ) ∈ R p 2 denotes an unknown nonlinear part of the local models, which can increase the model precision and reduce the computation burden as well as the numbers of fuzzy rules (Moodi and Farrokhi, 2014, 2015). In this paper, f s ( t ) ∈ R m denotes the sensor fault vector, w ( k ) ∈ …”

Section: Definitions and Formulations Of The Systemsmentioning

confidence: 99%

A novel double-level observer-based fault estimation for Takagi–Sugeno fuzzy systems with unknown nonlinear dynamics

Sun

Zhang

Han

et al. 2019

Transactions of the Institute of Measurement and Control

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In this paper we investigate the fault estimation problem against local unknown nonlinear dynamics, sensor and actuator faults for a class of Takagi–Sugeno (T-S) fuzzy systems. In addition, the exogenous disturbances and measurement noise are considered, which are presented in the operation of the systems and are various and independent of the systems. A novel double-level observer is designed to estimate the system states and faults. Compared with the current research results, the proposed observer has a wider range of application. By designing a fuzzy augmented system and a Kalman filter as the first-level observer, the estimations of system states, sensor faults and actuator faults can be obtained simultaneously. The second-level observer can estimate the unknown nonlinear dynamic function by establishing generalized fuzzy hyperbolic model. The robust stability of the estimation error systems is considered by H∞ performance. Finally, three simulation examples are provided to demonstrate the effectiveness of the proposed fault estimation method.

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Section: Definitions and Formulations Of The Systemsmentioning

confidence: 99%

A novel double-level observer-based fault estimation for Takagi–Sugeno fuzzy systems with unknown nonlinear dynamics

Sun

Zhang

Han

et al. 2019

Transactions of the Institute of Measurement and Control

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“…Indeed, the rotor speed figures simultaneously as a parameter and as an immeasurable decision variable. Thus, the problem could not be treated as an immeasurable state estimation issue [23,25,26]. This challenge was addressed in this work and forms part of the following sections.…”

Section: Ts Fuzzy Model Of Induction Machinementioning

confidence: 99%

Fuzzy speed estimation in the case of sensorless induction machine vector control

Bahloul¹,

Souissi²,

Chaabane³

et al. 2016

Turk J Elec Eng & Comp Sci

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This paper deals with the problem of sensorless indirect field oriented control of an induction machine. Based on the fourth-order nonlinear model, we propose a novel structure of a full-order Takagi-Sugeno (TS) adaptive observer, through which we ensure the estimation of IM states and the rotor speed, which is considered as an immeasurable premise variable. This approach satisfies global stability for all operating conditions and operates under unknown applied load torque. In this work, a TS fuzzy model is presented to approximate a nonlinear IM model in the stationary rotating reference frame. A TS adaptive observer is then described. We then design observer gains to guarantee specified observer dynamic performances through a D-stability analysis. Meanwhile, a TS-based scheme is proposed for speed estimation. The synthesis is based on sufficient conditions that are developed and formulated into linear matrix inequalities terms. Simulation and experimental results are given to show the effectiveness of the proposed method.

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“…In the field of control engineering [1], the fuzzy logic control has been regarded as a powerful tool to anlyze the performance of the nonlinear systems. Among various fuzzy models, Takagi-Sugeno (T-S) fuzzy model is widely accepted as a simple but effective model to describe the behaviors of uncertain nonlinear systems, where a set of fuzzy IF-THEN rules is introduced to describe the local linear input-output relations of a nonlinear system [14][15][16][17][18][19][20][21][22][23][24]. On the other hand, over the past decades, the stability and stabilization performance of uncertain T-S fuzzy systems turned out to be a more sensitive research theme [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Robust stabilization of T-S fuzzy systems via improved integral inequality

et al. 2021

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This paper addressed the robust stabilization performance of Tagaki-Sugeno (T-S) fuzzy systems under a state feedback controller. To attain this, an integral inequality is proposed by rearranging the quadratic matrix-vector form combined with Jensen's inequality to handle the single integral terms obtained by taking the derivative of the concerned Lyapunov-Krasovskii functional (LKF). By employing this integral inequality and by using integral inequality techniques, some improved delay dependent stability and stabilization results are established in terms of linear matrix inequalities for the proposed T-S fuzzy model. Finally some numerical examples are provided to facilitate the feasibility and less conservativeness of the proposed theoretical results.

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On observer-based controller design for Sugeno systems with unmeasurable premise variables

Cited by 57 publications

References 36 publications

A novel double-level observer-based fault estimation for Takagi–Sugeno fuzzy systems with unknown nonlinear dynamics

A novel double-level observer-based fault estimation for Takagi–Sugeno fuzzy systems with unknown nonlinear dynamics

Fuzzy speed estimation in the case of sensorless induction machine vector control

Robust stabilization of T-S fuzzy systems via improved integral inequality

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