Adaptive Output-Feedback Fuzzy Tracking Control for a Class of Nonlinear Systems

Zhou, Qi; Shi, Peng; Jin-jun, LU; Xu, Shengyuan

doi:10.1109/tfuzz.2011.2158652

Cited by 327 publications

(152 citation statements)

References 26 publications

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“…Comparing both the IT2 FMB control systems, the one in (29) demonstrates some favourable properties to facilitate the stability analysis: 1) The partial information of h ijl (x(t)) and h ijl (x(t)) is extracted and represented by the constant scalars δ iji1i2···inkl and δ iji1i2···inkl , which are brought to the stability conditions. 2) Referring to (25) and (26), the cross terms, ∏ n r=1 v rirkl (x r (t)), are independent of i and j and, thus, can be collected in the stability analysis.…”

Section: Remarkmentioning

confidence: 99%

“…With the consideration of the information of type-1 membership functions, stability conditions can be further relaxed [20]- [22]. Also, the fuzzy control concept were extended to other stability/control problems such as output feedback control [23], sampled-data control [24], control systems with time delay [8], tracking control [25], large scale fuzzy systems [26] and even for fuzzy neural networks [27].…”

mentioning

confidence: 99%

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Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

Lam

Deters

et al. 2014

IEEE Trans. Ind. Electron.

312

192

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Abstract-This paper focuses on designing interval type-2 (IT2) control for nonlinear systems subject to parameter uncertainties. To facilitate the stability analysis and control synthesis, an IT2 T-S fuzzy model is employed to represent the dynamics of nonlinear systems of which the parameter uncertainties are captured by IT2 membership functions characterized by the lower and upper membership functions. A novel IT2 fuzzy controller is proposed to perform the control process, where the membership functions and number of rules can be freely chosen and different from those of the IT2 T-S fuzzy model. Consequently, the IT2 fuzzymodel-based (FMB) control system is with imperfectly matched membership functions, which hinders the stability analysis. To relax the stability analysis for this class of IT2 FMB control systems, the information of footprint of uncertainties, and the lower and upper membership functions are taken into account for the stability analysis. Based on the Lyapunov stability theory, some stability conditions in terms of linear matrix inequalities are obtained to determine the system stability and achieve the control design. Finally, simulation and experimental examples are provided to demonstrate the effectiveness and the merit of the proposed approach.Index Terms-Fuzzy control, imperfect premise matching, interval type-2 fuzzy control, stability analysis.

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Section: Remarkmentioning

confidence: 99%

mentioning

confidence: 99%

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

Lam

Deters

et al. 2014

IEEE Trans. Ind. Electron.

312

192

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show abstract

“…The IT2 fuzzy controller in (4) is said to be a reliable mixed H 2 =H 1 fuzzy SOF controller for IT2 FMB system (2) if the closed-loop system (2) is reliable stable and satisfies the definitions in (10) and (11).…”

Section: Definitionmentioning

confidence: 99%

“…Recently, there has been significant interesting on the modeling, stability analysis, and controller synthesis problems for nonlinear systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Sensor and actuator failures often occur in the practical systems due to the sensors or actuators aging, zero shift, electromagnetic interference, and so forth, sensor failures, which may cause intolerable system performance [15].…”

Section: Introductionmentioning

confidence: 99%

Static output‐feedback control for interval type‐2 discrete‐time fuzzy systems

Gao

Chadli

et al. 2014

Complexity

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This article investigates the problem of reliable mixed H 2 =H 1 control for discrete-time interval type-2 (IT2) fuzzy model-based systems via static output-feedback (SOF) control method. The number of fuzzy rules and the membership functions for the SOF controller are different from those for the plant. A sufficient criterion of reliable stability with mixed H 2 =H 1 performance is derived for the closed-loop system with sensor failure. The SOF controller is designed for two different cases (known sensor failure case and unknown sensor failure case) to guarantee the reliable stability with mixed H 2 =H 1 performance. Moreover, novel criteria are presented to obtain the optical H 2 performance for the closed-loop system. Finally, an example is used to verify the effectiveness of the proposed design scheme. V C 2014 Wiley Periodicals, Inc. Complexity 21: 74-88, 2016

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“…Since the pioneering work from Zadeh in 1965 [14], fuzzy science has received more and more attentions, corresponding researches can be seen in [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] and the references therein. Especially in 1992, Wang proved that fuzzy systems are universal approximators [30], fuzzy method soon became a powerful approximation tool and has wide application areas, SMC is one of them.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy sliding mode control design for a class of disturbed systems

Wang

Shi

Karimi

2014

Journal of the Franklin Institute

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This paper discusses the problem of the fuzzy sliding mode control for a class of disturbed systems. First, a fuzzy auxiliary controller is constructed based on a feedback signal not only to estimate the unknown control term, but also participates in the sliding mode control due to the fuzzy rule employed. Then, we extend our theory into the cases, where some kind of system information can not be obtained, for better use of our theoretical results in real engineering. Finally, some typical numerical examples are included to demonstrate the effectiveness and advantage of the designed sliding mode controller. © 2013 The Franklin Institute

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Adaptive Output-Feedback Fuzzy Tracking Control for a Class of Nonlinear Systems

Cited by 327 publications

References 26 publications

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

Static output‐feedback control for interval type‐2 discrete‐time fuzzy systems

Fuzzy sliding mode control design for a class of disturbed systems

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