Dynamic parallel distributed compensation for Takagi–Sugeno fuzzy systems: An LMI approach

Li, Jing; Wang, Hua O.; Niemann, D.; Tanaka, Kazuo

doi:10.1016/s0020-0255(99)00129-2

Cited by 126 publications

(52 citation statements)

References 15 publications

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“…Since the state x(k) is unmeasurable, we use the fuzzy dynamical output feedback controller scheme [15], [38] to construct the fuzzy DOFFTC for T-S fuzzy models as…”

Section: Fault Accommodation Designmentioning

confidence: 99%

Integrated Fault Estimation and Accommodation Design for Discrete-Time Takagi–Sugeno Fuzzy Systems With Actuator Faults

Jiang

Zhang

Shi

2011

IEEE Trans. Fuzzy Syst.

197

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Abstract-This paper addresses the problem of integrated robust fault estimation (FE) and accommodation for discretetime Takagi-Sugeno (T-S) fuzzy systems. First, a multiconstrained reduced-order FE observer (RFEO) is proposed to achieve FE for discrete-time T-S fuzzy models with actuator faults. Based on the RFEO, a new fault estimator is constructed. Then, using the information of on-line FE, a new approach for fault accommodation based on fuzzy dynamic output feedback is designed to compensate for the effect of faults by stabilizing the closed-loop systems. Moreover, the RFEO and the dynamic output feedback fault tolerant controller are designed separately, such that their design parameters can be calculated readily. Simulation results are presented to illustrate our contributions.

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“…Since the state x(k) is unmeasurable, we use the fuzzy dynamical output feedback controller scheme [15], [38] to construct the fuzzy DOFFTC for T-S fuzzy models as…”

Section: Fault Accommodation Designmentioning

confidence: 99%

Integrated Fault Estimation and Accommodation Design for Discrete-Time Takagi–Sugeno Fuzzy Systems With Actuator Faults

Jiang

Zhang

Shi

2011

IEEE Trans. Fuzzy Syst.

197

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“…The design of a linear controller for each linear subsystem produces a parallel distributed controller for the whole system 30 . Model-based fuzzy control methods have the advantages of guaranteeing stability and robustness of the closed-loop system and, at the same time, producing desirable transient performance 31 .…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Control of a chain pendulum: A fuzzy logic approach

Aranda-Escolástico¹,

Guinaldo²,

Santos³

et al. 2016

IJCIS

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In this paper we present a real application of computational intelligence. Fuzzy control of a non-linear rotary chain pendulum is proposed and implemented on real prototypes. The final aim is to obtain a larger region of attraction for the stabilization of this complex system, that is, a more robust controller. As it is well-known, fuzzy logic exploits the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost when dealing with complex systems. In this case, the control strategy is based on a Takagi-Sugeno fuzzy model of the strongly non-linear multivariable system. Simulation and experimental results on the real plant have been obtained and tested in a rotary inverted pendulum and in a double rotary inverted pendulum. They have been compared to other feedback control strategies such as Full State Feedback or Linear Quadratic Regulator with encouraging results. Fuzzy control allows to enlarge the stability region of control. Indeed, the region of attraction and therefore the stabilization has been enlarged up to over 17% for the real system.

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“…In the last years, attractive stability results for nonlinear systems using Takagi-Sugeno (T-S) fuzzy systems [1] based on the theory of stability of nonlinear systems appeared in Feng et al [2] and Cao et al [3]. In the same context, stability conditions using LMIs were given in Hong and Langari [4], Li et al [5] and Wang et al [6]. Following this trend, the robust stabilization of uncertain nonlinear systems became an active field of research wherein available robust control techniques for uncertain #593/04.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed cost fuzzy controllers for a class of uncertain nonlinear dynamic systems

Arrifano¹,

Oliveira²

2005

Mat. apl. comput.

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Abstract. Stability analysis and a systematic robust fuzzy control design for uncertain nonlinear systems with polytopic uncertainties are presented. The approach given uses the T-S fuzzy systems which are represented by a family of local uncertain linear systems with aggregation. The resulting fuzzy controller is a blending of state feedback gains obtained from a guaranteed cost control (GCC) problem. A sufficient condition for the robust stability of the feedback T-S fuzzy system with uncertainties using a quadratic Lyapunov function is given. A feature of the proposed approach is that an upper bound on the guaranteed cost is minimized by solving an optimization problem with linear matrix inequalities (LMIs).Mathematical subject classification: 93C42, 93B51, 34D20.

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Dynamic parallel distributed compensation for Takagi–Sugeno fuzzy systems: An LMI approach

Cited by 126 publications

References 15 publications

Integrated Fault Estimation and Accommodation Design for Discrete-Time Takagi–Sugeno Fuzzy Systems With Actuator Faults

Integrated Fault Estimation and Accommodation Design for Discrete-Time Takagi–Sugeno Fuzzy Systems With Actuator Faults

Control of a chain pendulum: A fuzzy logic approach

Guaranteed cost fuzzy controllers for a class of uncertain nonlinear dynamic systems

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