Evolved Fuzzy Control System for a Steam Generator

Hossu, Daniela; Făgărăşan, Ioana; Hossu, Andrei; Iliescu, Sergiu Stelian

doi:10.5772/34497

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…26,27 Employment of NF modelling to different SGP plants is also extensively studied in some previous works. 26,[28][29][30][31][32][33][34] In model-based predictive control and model-based fault diagnosis frameworks, the computational complexities of the established non-linear models represent the most concern. 1,2 Hence, such a demand necessitates the use of non-linear model structures that are as computationally inexpensive as possible in order to diminish the overall complexity of model-based control and diagnosis algorithms.…”

Section: Introductionmentioning

confidence: 99%

Intelligent computationally efficient modelling of multi-input multi-output non-linear dynamical process plants: An industrial steam generator case study

Nozari

Banadaki

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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Most of the process plants have intrinsic non-linear, time-varying, non-minimum phase and coupling characteristics that result in a highly time-consuming and tedious task to derive complicated non-linear dynamic equations governing on such types of processes. On the other hand, even though such equations can be analytically extracted, they have not worked out in most cases yet. To find a simple fix that can address these hurdles associated with analytical modelling, two types of non-analytical models, namely, simulator and predictor, are proposed based on a computationally efficient technique so-called locally linear neuro-fuzzy modelling. That is, a simulator model as well as a specially structured long-term predictor model that is built based on a sequential arrangement of single-stage predictor models is presented for multi-input multi-output non-linear dynamical process plants and feasibly applied to a real water-tube steam generator for the first time. An adaptive evolving algorithm named linear model tree contributes to estimate the parameters of neuro-fuzzy simulator and predictor models. Furthermore, an order selection method based on Lipschitz theory, whose merit is being totally independent from developing any model prior to commencing tedious modelling trials, is also proposed for the first time by defining a modified Lipschitz index to remedy the order determination problem within the very first steps of black-box non-linear system identification. The blend of such a fully modelling-independent order selection algorithm and a unique type of computationally inexpensive non-linear neuro-fuzzy model lessens the overall computational burden of modelling procedure that represents a great concern in a black-box system identification approach. The recorded data from a real water-tube steam generator operating at Abbot Power plant unit of Champaign, IL, were exploited to carry out experimental modelling in order to reveal the pros and cons of the presented models.

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Section: Introductionmentioning

confidence: 99%

Intelligent computationally efficient modelling of multi-input multi-output non-linear dynamical process plants: An industrial steam generator case study

Nozari

Banadaki

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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Quasilinear Fuzzy Polytopic Predictive Control for the Traction Converters in the High-Speed Train

Tasiu,

Cai,

Bamisile

et al. 2024

IEEE Trans. Veh. Technol.

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Axiomatic Theory of Complex Fuzzy Logic and Complex Fuzzy Classes

Tamir

Kandel

2011

INT J COMPUT COMMUN

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Complex fuzzy sets, classes, and logic have an important role in applications, such as prediction of periodic events and advanced control systems, where several fuzzy variables interact with each other in a multifaceted way that cannot be represented effectively via simple fuzzy operations such as union, intersection, complement, negation, conjunction and disjunction. The initial formulation of these terms stems from the definition of complex fuzzy grade of membership. The problem, however, with these definitions are twofold: 1) the complex fuzzy membership is limited to polar representation with only one fuzzy component.2) The definition is based on grade of membership and is lacking the rigor of axiomatic formulation. A new interpretation of complex fuzzy membership enables polar and Cartesian representation of the membership function where the two function components carry uncertain information. Moreover, the new interpretation is used to define complex fuzzy classes and develop an axiomatic based theory of complex propositional fuzzy logic. Additionally, the generalization of the theory to multidimensional fuzzy grades of membership has been demonstrated. In this paper we propose an axiomatic framework for first order predicate complex fuzzy logic and use this framework for axiomatic definition of complex fuzzy classes. We use these rigorous definitions to exemplify inference in complex economic systems. The new framework overcomes the main limitations of current theory and provides several advantages. First, the derivation of the new theory is based on axiomatic approach and does not assume the existence of complex fuzzy sets or complex fuzzy classes. Second, the new form significantly improves the expressive power and inference capability of complex fuzzy logic and class theory. The paper surveys the current state of complex fuzzy sets, complex fuzzy classes, and complex fuzzy logic; and provides an axiomatic basis for first order predicate complex fuzzy logic and complex class theory.

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Evolved Fuzzy Control System for a Steam Generator

Cited by 3 publications

References 29 publications

Intelligent computationally efficient modelling of multi-input multi-output non-linear dynamical process plants: An industrial steam generator case study

Intelligent computationally efficient modelling of multi-input multi-output non-linear dynamical process plants: An industrial steam generator case study

Quasilinear Fuzzy Polytopic Predictive Control for the Traction Converters in the High-Speed Train

Axiomatic Theory of Complex Fuzzy Logic and Complex Fuzzy Classes

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