Robust H ∞ -tracking control design for T–S fuzzy systems with partly immeasurable premise variables

Dong, Jie; Wang, Shixin

doi:10.1016/j.jfranklin.2017.01.016

Cited by 26 publications

(15 citation statements)

References 34 publications

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“…The main characteristic of the T-S fuzzy model approach is that: the fuzzy model consists of some linear subsystems, which are connected by IF-THEN rules. Due to the characteristic of the T-S fuzzy model approach, some excellent methods in linear control theory can be applied for the analysis and synthesis of non-linear systems [20]. For this reason, the T-S fuzzy model approach has attracted great attention and many interesting problems about the approach have been investigated, such as tracking control [21,22], local stabilization [23,24], passive control [25,26], state estimation [27,28], event-triggered control [29,30].…”

Section: Introductionmentioning

confidence: 99%

A new fault tolerant control scheme for non‐linear systems by T‐S fuzzy model approach

Yang

et al. 2021

IET Control Theory & Appl

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This paper is concerned with the problem of fault tolerant control for non-linear systems with sensor faults based on the T-S fuzzy model approach. First, a fault estimation (FE) observer with L ∞ technique is designed to estimate the fault. Second, by using the fault estimation information, which are provided by the fault estimation observer, a novel fault tolerant control scheme with a fault compensation mechanism in the premise part of the fuzzy controller is presented to compensate the effect of the fault and stabilize the system. Different from the existing methods, where the fault compensation mechanism is only established in the consequent part of the controller, in the presented method, the fault compensation mechanism is established in the consequent part as well as the premise part of the fuzzy controller. Then, by using the fault compensation mechanism, the bound of the deviation between the normalized membership functions of the fuzzy model and those of the controller can be reduced such that the fault tolerant control design can be relaxed. In the end, an example is given to illustrate the validity of the developed fault tolerant control scheme.

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Section: Introductionmentioning

confidence: 99%

A new fault tolerant control scheme for non‐linear systems by T‐S fuzzy model approach

Yang

et al. 2021

IET Control Theory & Appl

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“…The most popular method to deal with such control design problems is the backstepping-based adaptive control technique in conjunction with fuzzy logic systems (FLSs) or neural networks, and plentiful of results have been reported for various types of nonlinear systems. [1][2][3][4][5][6][7][8][9][10][11][12] Likewise, a novel adaptive event-triggered strategy has been proposed to control a family of pure-feedback nonlinear systems in the work of Li et al 13 Hu et al 14 have thus applied control techniques to a hypersonic flight vehicle system, and both parametric uncertainties and unmodeled dynamics were also taken into consideration. In the work of Zhai et al, 15 robust stabilization of nonlinear systems with unstable zero dynamics has been investigated, and the adaptive tracking control problem for nonlinear nonstrict-feedback systems has been addressed in the work of Chen et al 16 In light of a nonsmooth Lyapunov function, the literature 17 has developed the asymptotic tracking control design for a family of switched nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive tracking control of stochastic switched nonlinear systems with unknown dead‐zone output

Liu

2021

Intl J Robust & Nonlinear

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In this article, the problem of adaptive output feedback control is carried out for stochastic switched nonlinear systems with dead-zone output and asymmetric tracking constraints. A type of smooth approximate model is proposed, which unfastens the nondifferentiable phenomena of dead-zone output.The Nussbaum-type function is utilized to develop the control design of the unknown dead-zone output. Subsequently, an improved first-order filter is cooperated into the barrier Lyapunov function analysis, which avoids the repeated derivative problem of virtual control inputs in the backstepping design procedure and completes the control design successfully. The proposed constructive control algorithm can achieve the control goal well. Finally, a numerical example with comparison results is given to further verify the validity of the new control tactic.

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“…Hence Takagi‐Sugeno (T‐S) fuzzy system models 1,2 had been proposed to represent some nonlinear systems in recent years. T‐S fuzzy system models are used to solve some practical problems in control dynamic systems; such as induction motor drive control 3 ; networked control 4,5 ; neural network predictive control 6 ; nonlinear modeling 7 ; predictive control for a diesel engine 8 ; robust H ∞ ‐tracking control 9 ; sliding mode‐like learning control 10 . This approach provides a bridge between the linear control theory and the fuzzy logic concept.…”

Section: Introductionmentioning

confidence: 99%

“…The H ∞ performance of systems was used to inspect the effect of regulated output with respect to disturbance input and guarantee that the closed‐loop system is stable in recent years 9,12,13,23,38‐44 . On the other hand, H 2 performance of systems was an another requirement and applied to minimize a quadratic performance index about the initial state of system under no disturbance input 45 .…”

Section: Introductionmentioning

confidence: 99%

Robust mixed performance control of uncertain T‐S fuzzy time‐delay systems with aperiodic sampled‐data input

Lien

Hou

et al. 2020

Optim Control Appl Methods

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In this paper, the robust sampled‐data input mixed H2/H∞ performance control of uncertain T‐S fuzzy systems with time delay and aperiodic sampled‐data state feedback input is considered. Some delay‐dependent linear matrix inequality‐based criteria are proposed to guarantee the H2 performance by the design of robust H∞ control. The novel aperiodic sampled‐data parallel distributed compensator is developed to achieve the mixed performance. Finally, we demonstrate two examples to show the main proposed results. A numerical example illustrates that the developed results are less conservative than published ones. We also provide a practical system about ship motion to show the simulation and verification of our contribution.

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Robust H ∞ -tracking control design for T–S fuzzy systems with partly immeasurable premise variables

Cited by 26 publications

References 34 publications

A new fault tolerant control scheme for non‐linear systems by T‐S fuzzy model approach

A new fault tolerant control scheme for non‐linear systems by T‐S fuzzy model approach

Adaptive tracking control of stochastic switched nonlinear systems with unknown dead‐zone output

Robust mixed performance control of uncertain T‐S fuzzy time‐delay systems with aperiodic sampled‐data input

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