Fuzzy model-reference adaptive control

Yin, Tang-Kai; Lee, C.S.G.

doi:10.1109/21.478448

Cited by 79 publications

(10 citation statements)

References 14 publications

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“…Adaptive controllers with composite adaptive laws, based on both the tracking and model prediction error, have been also proposed in the literature [64,25].…”

Section: Indirect and Direct Adaptive Fuzzy Controlmentioning

confidence: 99%

Perspectives of fuzzy systems and control

Sala

Guerra

Babuška

2005

Fuzzy Sets and Systems

365

143

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Although fuzzy control was initially introduced as a model-free control design method based on the knowledge of a human operator, current research is almost exclusively devoted to model-based fuzzy control methods that can guarantee stability and robustness of the closed-loop system. State-of-the-art techniques for identifying fuzzy models and designing model-based controllers are reviewed in this article. Attention is also paid to the role of fuzzy systems in higher levels of the control hierarchy, such as expert control, supervision and diagnostic systems. Open issues are highlighted and an attempt is made to give some directions for future research.

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“…Adaptive controllers with composite adaptive laws, based on both the tracking and model prediction error, have been also proposed in the literature [64,25].…”

Section: Indirect and Direct Adaptive Fuzzy Controlmentioning

confidence: 99%

Perspectives of fuzzy systems and control

Sala

Guerra

Babuška

2005

Fuzzy Sets and Systems

365

143

View full text Add to dashboard Cite

show abstract

“…Then a fuzzy sliding controller was added into the adaptive controller for compensating the uncertainties and smoothing the control signal. Yin and Lee [14] proposed a fuzzy model-reference adaptive control structure by using the fuzzy basis function expansion [8] to represent the parameter information of the system model. Hence how to synthesize the advantages of previous works, and release the strictly inherent limitations for handling system unknown disturbances and quickly time-varying parameters in designing a novel MRAFSC controller is the objective of this research.…”

Section: Introductionmentioning

confidence: 99%

An adaptive fuzzy controller for robot manipulators

Chiou

Huang

2005

Mechatronics

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“…The shapes of the membership functions of the fuzzy controller were adjusted according to the adaptive rule derived based on the mathematical model of the plant. Model-reference adaptive [22], [60], [81] and self-organizing adaptive [33] fuzzy control approach were also reported. By applying adaptive fuzzy controllers, the stability of the closed-loop system is usually guaranteed, and the system is robust to the parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Stable and robust fuzzy control for uncertain nonlinear systems

Lam

Leung

Tam

2000

IEEE Trans. Syst., Man, Cybern. A

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Abstract-This paper presents the stability and robustness analysis for multivariable fuzzy control systems subject to parameter uncertainties based on a single-grid-point (SGP) approach. To perform the analysis, we represent a multivariable nonlinear system using a TS-fuzzy plant model. Three design approaches of fuzzy controllers are introduced to close the feedback loop. By estimating the matrix measures of the system parameters and parameter uncertainties, stability and robustness conditions for different cases are derived. Application examples will be given to show the design procedures and the merits of the proposed fuzzy controller.Index Terms-fuzzy control, nonlinear system robustness, parameter uncertainty, stability.

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Fuzzy model-reference adaptive control

Cited by 79 publications

References 14 publications

Perspectives of fuzzy systems and control

Perspectives of fuzzy systems and control

An adaptive fuzzy controller for robot manipulators

Stable and robust fuzzy control for uncertain nonlinear systems

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