Design of an adaptive interval type-2 fuzzy logic controller for the position control of a servo system with an intelligent sensor

Kayacan, Erdal; Kaynak, Okyay; Abiyev, Rahib H.; Tørresen, Jim; Høvin, M.; Glette, Kyrre

doi:10.1109/fuzzy.2010.5584629

Cited by 24 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sliding mode control adaptation laws in [48] for the consequent parameters in fuzzy logic controllers are used. Another possibility is to use gradient descent based training algorithms as in [49].…”

Section: Control Schemementioning

confidence: 99%

Type-2 fuzzy elliptic membership functions for modeling uncertainty

Kayacan

Sarabakha

Coupland

et al. 2018

Engineering Applications of Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance with existing type-2 fuzzy MFs. Furthermore, fuzzy arithmetic operations are also investigated, and our finding is that the elliptic MF has similar features to the Gaussian and triangular MFs in addition and multiplication operations. Moreover, we have tested the prediction capability of elliptic MFs using interval type-2 fuzzy logic systems on oil price prediction problem for a data set from 2nd Jan 1985 till 25th April 2016. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem of a quadrotor. We believe that the results of this study will open the doors to elliptic MFs' wider use of real-world identification and control applications as the proposed MF is easy to interpret in addition to its unique features.

show abstract

Section: Control Schemementioning

confidence: 99%

Type-2 fuzzy elliptic membership functions for modeling uncertainty

Kayacan

Sarabakha

Coupland

et al. 2018

Engineering Applications of Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a type‐2 fuzzy inference system (FIS), type‐2 fuzzy sets are used in the antecedent and/or consequent parts of all or some of its fuzzy rules. Type‐2 FISs have gained successful applications in various areas where uncertainties occur, such as in diagnostic medicine and in intelligent signal processing …”

Section: Introductionmentioning

confidence: 99%

Type-Reduction of General Type-2 Fuzzy Sets: The Type-1 OWA Approach

Chiclana

Zhou

2013

Int. J. Intell. Syst.

View full text Add to dashboard Cite

For general type‐2 fuzzy sets, the defuzzification process is very complex and the exhaustive direct method of implementing type‐reduction is computationally expensive and turns out to be impractical. This has inevitably hindered the development of type‐2 fuzzy inferencing systems in real‐world applications. The present situation will not be expected to change, unless an efficient and fast method of deffuzzifying general type‐2 fuzzy sets emerges. Type‐1 ordered weighted averaging (OWA) operators have been proposed to aggregate expert uncertain knowledge expressed by type‐1 fuzzy sets in decision making. In particular, the recently developed alpha‐level approach to type‐1 OWA operations has proven to be an effective tool for aggregating uncertain information with uncertain weights in real‐time applications because its complexity is of linear order. In this paper, we prove that the mathematical representation of the type‐reduced set (TRS) of a general type‐2 fuzzy set is equivalent to that of a special case of type‐1 OWA operator. This relationship opens up a new way of performing type reduction of general type‐2 fuzzy sets, allowing the use of the alpha‐level approach to type‐1 OWA operations to compute the TRS of a general type‐2 fuzzy set. As a result, a fast and efficient method of computing the centroid of general type‐2 fuzzy sets is realized. The experimental results presented here illustrate the effectiveness of this method in conducting type reduction of different general type‐2 fuzzy sets.

show abstract

“…Most researchers concentrate exclusively on the comparatively simple interval secondary membership functions [2], for which an increasing number of applications are being developed [3], [4], [5], [6], [7], [8], [9]. The capability of the generalised type-2 paradigm to handle uncertainty is explored in [10].…”

Section: Introductionmentioning

confidence: 99%