Fuzzy model reference adaptive control

Goléa, N.; Goléa, A.; Benmahammed, K.

doi:10.1109/tfuzz.2002.800694

Cited by 100 publications

(39 citation statements)

References 24 publications

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“…3. The plant control u is inferred from the two state variables, error ðeÞ and change in error De (Goléa et al 2002;Senthil Kumar et al 2008).…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

“…The control rules are designed to assign a fuzzy set of the control input u for each combination of fuzzy sets of ðeÞ and De (Chung et al 1999;Goléa et al 2002). Table 1 shows one of possible control rule base.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

“…In this paper, the triangular membership function, the max-min reasoning method, and the center of gravity defuzzification method are used, as those methods are most frequently used frequently in the literature (Goléa et al 2002;Senthil Kumar et al 2008). …”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

See 2 more Smart Citations

Sensorless fuzzy sliding mode control for permanent magnet synchronous motor fed by AC/DC/AC converter

Benchabane

Titaouine

Bennis

et al. 2012

Int J Syst Assur Eng Manag

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In this paper a systematic fuzzy sliding mode controller is presented for permanent magnet synchronous motor (PMSM) sensorless drives. The fuzzy sliding mode controller is designed based on input-output feedback linearization control technique. The extended Kalman filter is used to estimate the speed, position and load torque. The PMSM is fed from indirect power electronics converter. This indirect converter is controlled by the sliding mode technique. This control technique allows the minimization of harmonics introduced by the line converter, including the control of power factor and DC-link voltage. The robustness of the overall system is studied using simulation for different operating modes and varied parameters.

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“…3. The plant control u is inferred from the two state variables, error ðeÞ and change in error De (Goléa et al 2002;Senthil Kumar et al 2008).…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

Section: Fuzzy Logic Controllermentioning

confidence: 99%

See 1 more Smart Citation

Sensorless fuzzy sliding mode control for permanent magnet synchronous motor fed by AC/DC/AC converter

Benchabane

Titaouine

Bennis

et al. 2012

Int J Syst Assur Eng Manag

View full text Add to dashboard Cite

show abstract

“…Even though Zadeh proposed the type-2 fuzzy sets in 1976; however, applications using type-1 fuzzy sets are much more than on type-2 fuzzy sets for decades [63]. Recently, more and more researchers around the world are studying about type-2 fuzzy sets and systems for various applications [64][65][66][67][68][69][70][71][72]. Different from the type-1 fuzzy set, the membership function of a general type-2 fuzzy set is three-dimensional, where the third dimension is the value of the membership function at each point on its two-dimensional domain that is called its footprint of uncertainty (FOU).…”

Section: Introductionmentioning

confidence: 99%

Vibration Control Design for a Plate Structure with Electrorheological ATVA Using Interval Type-2 Fuzzy System

Lin¹,

Lee²,

Liu³

2017

Preprint

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This study presents a vibration control using actively tunable vibration absorbers (ATVA) to suppress vibration of a thin plate. The ATVA's is made of a sandwich hollow structure embedded with the electrorheological fluid (ERF). ERF is considered to be one of the most important smart fluids and it is suitable to be embedded in a smart structure due to its controllable viscosity property.ERF's apparent viscosity can be controlled in response to the electric field and the change is reversible in 10 microseconds. Therefore, the physical properties of the ERF-embedded smart structure, such as the stiffness and damping coefficients, can be changed in response to the applied electric field. A mathematical model is difficult to be obtained to describe the exact characteristics of the ERF embedded ATVA because of the nonlinearity of ERF's viscosity. Therefore, a fuzzy modeling and experimental validations of ERF-based ATVA from stationary random vibrations of thin plates are presented in this study. Because Type-2 fuzzy sets generalize Type-1 fuzzy sets so that more modelling uncertainties can be handled, a semi-active vibration controller is proposed based on Type-2 fuzzy sets. To investigate the different performances by using different types of fuzzy controllers, the experimental measurements employing type-1 fuzzy and interval type-2 fuzzy controllers are implemented by the Compact RIO embedded system. The fuzzy modeling framework and solution methods presented in this work can be used for design, performance analysis, and optimization of ATVA from stationary random vibration of thin plates.

show abstract

“…Even though Zadeh proposed the type-2 fuzzy sets in 1976; however, applications using type-1 fuzzy sets are much more than on type-2 fuzzy sets for decades [40]. Recently, more and more researchers around the world are studying about type-2 fuzzy sets and systems for various applications [41][42][43][44][45][46][47][48][49]. Different from the type-1 fuzzy set, the membership function of a general type-2 fuzzy set is three-dimensional, where the third dimension is the value of the membership function at each point on its two-dimensional domain that is called its footprint of uncertainty (FOU).…”

Section: Introductionmentioning

confidence: 99%

Vibration Control Design for a Plate Structure with Electrorheological ATVA Using Interval Type-2 Fuzzy System

Lin¹,

Lee²,

Liu³

2017

Preprint

View full text Add to dashboard Cite

This study presents a vibration control using actively tunable vibration absorbers (ATVA) to suppress vibration of a thin plate. The ATVA's is made of a sandwich hollow structure embedded with the electrorheological fluid (ERF). ERF is considered to be one of the most important smart fluids and it is suitable to be embedded in a smart structure due to its controllable viscosity property. ERF's apparent viscosity can be controlled in response to the electric field and the change is reversible in 10 microseconds. Therefore, the physical properties of the ERF-embedded smart structure, such as the stiffness and damping coefficients, can be changed in response to the applied electric field. A mathematical model is difficult to be obtained to describe the exact characteristics of the ERF embedded ATVA because of the nonlinearity of ERF's viscosity. Therefore, a fuzzy modeling and experimental validations of ERF-based ATVA from stationary random vibrations of thin plates are presented in this study. Because Type-2 fuzzy sets generalize Type-1 fuzzy sets so that more modelling uncertainties can be handled, a semi-active vibration controller is proposed based on Type-2 fuzzy sets. To investigate the different performances by using different types of fuzzy controllers, the experimental measurements employing type-1 fuzzy and interval type-2 fuzzy controllers are implemented by the Compact RIO embedded system. The fuzzy modeling framework and solution methods presented in this work can be used for design, performance analysis, and optimization of ATVA from stationary random vibration of thin plates.

show abstract

Fuzzy model reference adaptive control

Cited by 100 publications

References 24 publications

Sensorless fuzzy sliding mode control for permanent magnet synchronous motor fed by AC/DC/AC converter

Sensorless fuzzy sliding mode control for permanent magnet synchronous motor fed by AC/DC/AC converter

Vibration Control Design for a Plate Structure with Electrorheological ATVA Using Interval Type-2 Fuzzy System

Vibration Control Design for a Plate Structure with Electrorheological ATVA Using Interval Type-2 Fuzzy System

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