A design neurofuzzy controller for level process control

Tipsuwanporn, V.; Koetsam-ang, N.; Kongratana, V.; Numomran, A.; Suesut, T.

doi:10.1109/ccece.2001.933573

Cited by 5 publications

(8 citation statements)

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“…The neuro-fuzzy controller, considered as a case study in this work, is an improved version of the definition presented in [22]. It represents an important alternative to onoff systems and PID controllers that are used in industrial processes such as biodiesel production but often perform poorly due to their inadequate handling of disturbances over time [13].…”

Section: Neuro-fuzzy Controllermentioning

confidence: 99%

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A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller

López-Muñoz,

Molina-Pérez,

Vega-Alvarado

et al. 2024

Applied Sciences

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This work presents a methodology to solve optimization problems with dynamic-size solution vectors containing continuous and integer variables. It is achieved by reformulating the original problem through a bilevel optimization approach and implementing metaheuristic techniques to solve it. In the selected case study, the optimization problem corresponds to tuning a neuro-fuzzy controller (NFC) that operates in a biodiesel production system for controlling temperature. The NFC performs well and is especially robust to disturbances, but due to its complexity, it is difficult to determine the best set of parameters for its use. This has led to biased searches based on criteria such as the experiences of designers. With the proposed method, it was possible to obtain a tuning that—when implemented in a simulation—led to results that surpassed those documented in the literature. Finally, the proposal offers flexibility for implementation with other controllers that have similar architectures and can be integrated into various other plants or processes.

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Section: Neuro-fuzzy Controllermentioning

confidence: 99%

“…On the other hand, the second optimization problem has an order constraint, and this means that any subset of ⃗ x must satisfy the following: (22),…”

Section: Optimizationmentioning

confidence: 99%

A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller

López-Muñoz,

Molina-Pérez,

Vega-Alvarado

et al. 2024

Applied Sciences

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“…There are many studies in the field of PID control of non-linear systems (Heong and Chong, 2006;Rajani et al, 2008;Yamamoto and Shah, 2007). Although PID control is one of the earlier methods in the control of chemical plant, artificial intelligence such as fuzzy sets and neural networks have been used to modify the PID, which led to better advanced control strategies (Aslam et al, 2011;Li et al 2007;Montiel et al, 2007;Tipsuwanporn et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Robust flow controller design and analysis for a chemical process

Alaei

Yazdizadeh

2014

Transactions of the Institute of Measurement and Control

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A large number of applications in industrial domains are concerned to slow processes, including flow, pressure, temperature and level control. Synthesis and control of such applications in the presence of uncertainty have been studied by many authors in recent decades. In this paper, a robust flow controller has been designed using H∞ techniques to define and regulate the flow of pipe. The most robust method is synthesized using a state-space approach by taking into account the structured (parametric) perturbations in the plant coefficients. The proposed approach ensures internal stability, satisfying both frequency and time domain requirements, and obtaining minimal performance H∞-norm of the closed-loop system. The simulations are carried out using MATLAB and the results are compared with the classical PID, linear–quadratic–Gaussian and H2 methods, and they indicate that the overall system output performance can be improved using the proposed H∞ robust controller.

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“…In [1] an industrial controller is interfaced with a neurofuzzy model for industrial processes control. The system has weak points in terms of interoperability and scalability, is that the code runs in an external computer which is connected to the industrial controller.…”

Section: Introductionmentioning

confidence: 99%

Decision making by using tree-like structures on industrial controllers

Moctezuma

Lobov

Lastra

2012

2012 Tenth International Conference on ICT and Knowledge Engineering

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The implementation of a decision making technique on industrial controllers is a constrained task since the controllers are programmed with languages standardized by the IEC 61131-3, which are meant for controlling processes, limiting the possibilities that traditional high level programming languages offer. So far most of the implementations of decision making techniques are done by executing the code on an external computer which is interfaced to an industrial controller. Decision tree structures can be used as a visual tool for designers to model the decision making of an industrial controller intuitively. Different situations can be modeled so that the controller knows how to act for each of them. Tree-like structures can be visualized as nested Boolean evaluations which can be easily converted to any of the IEC 61131-3 languages. Therefore the decision making logic can be executed directly on the industrial controller. The proof of concept is tested on a SCARA robot that has to execute different sequence of movements depending on the components state within a flexible manufacturing cell. Keywords-light decision making, IEC 611-31 compliant, industrial controller, decision tree structuresI.

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A design neurofuzzy controller for level process control

Cited by 5 publications

References 0 publications

A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller

A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller

Robust flow controller design and analysis for a chemical process

Decision making by using tree-like structures on industrial controllers

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