Indirect adaptive interval type-2 fuzzy control for nonlinear systems

Chafaa, Kheireddine; Saidi, Lamir; Ghanai, Mouna; Benmahammed, K.

doi:10.1504/ijmic.2007.014623

Cited by 31 publications

(5 citation statements)

References 16 publications

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“…The results are shown in figures (6)- (8). Before the test, the motor was turning during 15 minutes to warm up and hence getting a variation on the electrical resistance value.…”

Section: Resultsmentioning

confidence: 99%

“…However, the imprecision in such classical fuzzy system, which is sometimes called type-1 fuzzy logic system (T1FLS) is not fully exploited and can deliver a non-satisfactory performance. Practically, three ways of uncertainty can be observed in FLS: (1) the used words in antecedents and consequents of rules can mean different things to different people; (2) consequents obtained by polling a group of experts will often be different for the same rule and (3) both of training data and measurements used to activate the FLS are noisy [8].…”

Section: Introductionmentioning

confidence: 99%

“…Such advantage is quite important if we know that modelling of linguistic information and decision making represents the main application of fuzzy logic system (FLS). Several contributions present interesting results of applying T2FLS in control engineering [6,7,8,16,17,41,47]. Despite the efficiency of controllers based on T2FLS, their robustness is questionable against approximation error, external disturbance and other environmental effects.…”

Section: Introductionmentioning

confidence: 99%

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Robust Adaptive Type-2 Fuzzy Sliding Mode Control for Non-Linear uncertain SISO systems

Al-Khazraji

Aljebory

2014

JCET

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This paper deals with the synthesis of adaptive type-2 fuzzy logic controller for a class of single-input single-output system. Due to its ability in serving in universal approximation, the type-2 fuzzy logic system is used to approximate the unknown system dynamics, which will be adjusted according to the on-line adaptation laws deduced from the stability analysis. To ensure the closed loop system robustness, a modified sliding mode control signal is used. In this work, variable sliding surface is replaced by type-2 fuzzy logic system in order to reduce the starting energy without deteriorating the tracking performances. Furthermore, the knowledge of the upper bounds of the external disturbances and also the approximation errors is not needed. The global stability of the closed loop system is guaranteed in the sense of Lyapunov. As well, all signals involved are uniformly bounded. Finally, experiment results are presented to show the performance of the developed approach.

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“…The results are shown in figures (6)- (8). Before the test, the motor was turning during 15 minutes to warm up and hence getting a variation on the electrical resistance value.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Adaptive Type-2 Fuzzy Sliding Mode Control for Non-Linear uncertain SISO systems

Al-Khazraji

Aljebory

2014

JCET

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“…There are other techniques proposed in the literature for the purpose of enhancing the robustness, performance, and time execution, such as the MEKM algorithm [45], the EKM algorithm, the EIASC, and many others reported by Tai et al [46]. In our investigation the conventional Karnik-Mendal center of sets is used which is written as [47]:…”

Section: Type-2 Fuzzy Logic System Structurementioning

confidence: 99%

“…Phase 3: Through the use of the fundamental Karnik-Mendel center of sets type reduction [47] and the centroid defuzzification, the type-2 fuzzy filter is constructed around the set of N rules.…”

Section: Ifmentioning

confidence: 99%

TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

Ouali¹,

Ghanai²,

Chafaa³

2020

Self Cite

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A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals; these included real and artificial noise. For comparison purposes, both model and non-modelbased methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

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Indirect adaptive type-2 bionic fuzzy control

Zhang

Hua

2017

Appl Intell

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Indirect adaptive interval type-2 fuzzy control for nonlinear systems

Cited by 31 publications

References 16 publications

Robust Adaptive Type-2 Fuzzy Sliding Mode Control for Non-Linear uncertain SISO systems

Robust Adaptive Type-2 Fuzzy Sliding Mode Control for Non-Linear uncertain SISO systems

TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

Indirect adaptive type-2 bionic fuzzy control

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