Interval Type-2 fuzzy logic controller design for the speed control of DC motors

Hassani, Hossein; Zarei, Jafar

doi:10.1080/21642583.2015.1013644

Cited by 31 publications

(14 citation statements)

References 27 publications

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“…For example in [28] and [29], non-singleton type-2 FLSs are shown to effectively handle different levels of uncertainties in industrial use cases. It has also been shown that in the presence of perturbations, the generalized type-2 fuzzy controllers outperform their type-1 and interval type-2 counterparts [30], [31], and that interval type-2 NSFLSs outperform type-1 NSFLSs in different application domains [32], [33]. Incorporating other AI techniques with type-2 FLSs has also resulted in hybrid solutions for predicting the behaviour of non-linear complex systems (e.g., using neural networks in [34] and genetic algorithms in [35]).…”

Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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(2016) Improved uncertainty capture for non-singleton fuzzy systems. IEEE Transactions on Fuzzy Systems, 24 (6). pp. 1513 -1524 . ISSN 1941 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/45444/1/Improved%20Uncertainty%20Capture%20for %20Non-Singleton%20Fuzzy%20Systems.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 Improved Uncertainty Capture for Non-Singleton Fuzzy SystemsAmir Pourabdollah, Member, IEEE, Christian Wagner, Senior Member, IEEE, Jabran Hussain Aladi, Student Member, IEEE and Jonathan M. Garibaldi, Member, IEEE Abstract-In non-singleton fuzzy logic systems (NSFLSs), input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty (e.g., sensor noise). The performance of NSFLSs in handling such uncertainties depends on both: the appropriate modelling in the input fuzzy sets of the uncertainties present in the system's inputs, and on how the input fuzzy sets (and their inherent model of uncertainty) interact with the antecedent and thus affect the inference within the remainder of the NSFLS. This paper proposes a novel development on the latter. Specifically, an alteration to the standard composition method of type-1 fuzzy relations is proposed, and applied to build a new type of NSFLS. The proposed approach is based on employing the centroid of the intersection of input and antecedent sets as origin of the firing degree, rather than the traditional maximum of their intersection, thus making the NSFLS more sensitive to changes in the input's uncertainty characteristics. The traditional and novel approach to NSFLSs are experimentally compared for two well-known problems of Mackey-Glass and Lorenz chaotic time series predictions, where the NSFLSs' inputs have been perturbed with different levels of Gaussian noise. Experiments are repeated for system training under noisy and noise-free conditions. Analyses of the results show that the new method outperforms the traditional approach. Moreover, it is shown that while formally more complex, in practice, the new method has no significant computational overhead compared to the standard approach.

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Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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“…Membership functions are classified as type-1 and type-2, because defuzzification process is performed with membership functions and they differ structurally from each other. One of the most important features that separates T2FLC from T1FLC is the type reduction process [12][13][14][15][24][25]. Type-1 and type-2 triangular membership functions are also shown in Fig.…”

Section: Structure Of T2flcmentioning

confidence: 99%

“…The inference mechanism is a simulation of the human decision making process. Typereduction can be expressed as the process of finding the center of gravity of the output membership functions created for the system to be controlled [24][25].…”

Section: Structure Of T2flcmentioning

confidence: 99%

Speed Control of DC Motor Using Interval Type-2 Fuzzy Logic Controller

Açıkgöz

2018

ijisae

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Direct current (DC) motors are considerably utilized in many implementations requiring speed and position controls. The simplicity of DC motor speed control is also the main reason for its widespread use. Recently, with rapid developments in power electronics, microprocessors and semiconductor materials, many control structures are designed for DC motors. In this study, the speed control of the DC motor is carried out using Matlab/Simulink package program. Type-2 Fuzzy Logic controller (T2FLC) that has efficient performance in modelling uncertainties is proposed for DC motor speed control. The classical Proportional+Integral (PI) controller and T2FLC are connected to the speed control unit of DC motor. Simulation studies have also been realized under several operating conditions such as tracking reference speeds and load changes. According to the obtained simulation results, it has been observed that T2FLC has better results than the classical PI controller in operating conditions.

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“…The system structure of the QES is built entirely using a fuzzy logic interface system, which has been extensively used in a wide range of problem domains over the past decades [33–38]. This was done because QES uses linguistic terms instead of precise numerical values to describe system behaviour.…”

Section: Quality Evaluation Systemmentioning

confidence: 99%