Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers

Wu, Dongrui; Tan, Woei Wan

doi:10.1016/j.engappai.2005.12.011

Cited by 249 publications

(150 citation statements)

References 14 publications

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“…The FLSs are general knowledge base systems [122] with linguistic rules that can be constructed using the knowledge of experts in a given field. During more than four decades from the invention of fuzzy logic, great progress in using FLS has been achieved.…”

Section: Fuzzy Algoritm and Fuzzy Languagesmentioning

confidence: 99%

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Dzițac¹,

Filip²,

Manolescu³

2017

INT J COMPUT COMMUN

116

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Section: Fuzzy Algoritm and Fuzzy Languagesmentioning

confidence: 99%

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Dzițac¹,

Filip²,

Manolescu³

2017

INT J COMPUT COMMUN

116

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“…ITAE [33] is a method of analysing the error between the demand signal and the controllers. The integration of time means this objective function especially penalizes large settling times as well as large deviations from the demanded signal.…”

Section: Moga Overviewmentioning

confidence: 99%

“…The controlled variables for pitch, roll, and yaw are denoted as M cv , L cv , and N cv respectively. The controlled variable for the longitudinal dynamics is a combination of pitch rate and angle of attack (33) and the variables for the roll and yaw moments L and N are…”

Section: Inner Control Loopmentioning

confidence: 99%

Multi-objective evolutionary—fuzzy augmented flight control for an F16 aircraft

Stewart

Gladwin

Parr

et al. 2009

Proceedings of the Institution of Mechanical Engineers, Part G:

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Abstract:In this article, the multi-objective design of a fuzzy logic augmented flight controller for a high performance fighter jet (the Lockheed-Martin F16) is described. A fuzzy logic controller is designed and its membership functions tuned by genetic algorithms in order to design a roll, pitch, and yaw flight controller with enhanced manoeuverability which still retains safety critical operation when combined with a standard inner-loop stabilizing controller. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom motion base real-time flight simulator, and flight tested by a qualified pilot instructor.

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“…It has been shown in various works that IT2-FLCs achieve better control performance because of the additional degree of freedom provided by the Footprint of Uncertainty (FOU) in their Membership Functions (MFs) [3], [4]. Consequently, IT2-FLCs have attracted much research interest, especially in control applications [3][4][5][6][7][8][9]. In [10], [11], it has been reported that the IT2-FLCs are generally more robust than type-1 counterparts.…”

Section: Introductionmentioning

confidence: 99%

A big bang-big crunch optimization based approach for interval type-2 fuzzy PID controller design

Kumbasar

Hagras

2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-In this paper, we will present a big bang-big crunch optimization (BB-BC) based approach for the design of an interval type-2 fuzzy PID controller. The implemented global optimization algorithm has a low computational cost and a high convergence speed. As a consequence, the BB-BC method is a very efficient search algorithm when the number of the optimization parameters is relatively big. The optimized type-2 fuzzy controller is compared with PID and type-1 fuzzy PID controllers which were optimized with either the BB-BC optimization method or conventional design strategies. The paper will also show the effect the extra degrees of freedom provided by the antecedent interval type-2 fuzzy sets on the closed loop system performance. We will present a comparative study performed on the highly nonlinear cascaded tank process to show the superiority of the optimized interval type-2 fuzzy PID controller compared to its optimized PID, type-1 counterparts.

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Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers

Cited by 249 publications

References 14 publications

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Multi-objective evolutionary—fuzzy augmented flight control for an F16 aircraft

A big bang-big crunch optimization based approach for interval type-2 fuzzy PID controller design

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