Fuzzy modelling and model reference neural adaptive control of the concentration in a chemical reactor (CSTR)

Bahita, Mohamed; Belarbi, Khaled

doi:10.1007/s00146-018-0806-z

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…The extremely non-linear, complex, and usually unstable chemical reactions in these reactors are controlled to reduce the environmental impact of chemical waste. For instance, the instability could result in an abrupt temperature rise, a decrease in the rate of reaction, a safety breach, and eventually a shorter reactor life [3].…”

Section: Introductionmentioning

confidence: 99%

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FOPID Neural Network Controller Design for Nonlinear CSTR System

2023

IJIES

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In this paper, four control structures are provided for continuous ctirred-tank reactor (CSTR) system's fractional/integer order proportional integral derivative neural network controllers. The revised neural network weights and the controller's parameters are optimized using the optimization technique called ant colony optimization (ACO). The proposed controllers' resistance to changes in the initial state, outside disturbances, and parameter modifications is also tested. The fractional order proportional integral derivative neural network controllers provide the best assurance and also enhance the system's robustness to changes in the initial state, external disturbances, and parameter variations, according to the results of MATLAB code.The fractional order proportional integral derivative neural network controller1(FOPIDNNC1) is the best structure among all those with the minimum cost function equal to 0.011588 for the set-point variations, 0.015325 for uncertainty parameter, 0.018274 for disturbances rejection, and the best structure fractional order proportional integral derivative neural network controller3 (FOPIDNNC3) among all those with the minimum cost function equal to 0.008733 for tracking.

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Section: Introductionmentioning

confidence: 99%

“…At different operating points, the dynamic characteristics may also provide a shifting gain indication. These qualities make a process difficult to control and call for the employment of more sophisticated methods [3]. To address this issue, proposed a fractional order PID neural network controller.…”

Section: Introductionmentioning

confidence: 99%

FOPID Neural Network Controller Design for Nonlinear CSTR System

2023

IJIES

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“…The stabilization of nonlinear systems with complex or chaotic behavior has been analyzed from several years ago. In particular, the stabilization via nonlinear controllers with regulation purposes in chemical reactors is also a classical task for processes engineers; the control of chemical reactors has been done via linear PID, adaptive, predictive, I/O linearizing, fuzzy, neural, and optimal controllers with success [1][2][3][4][5][6]. A widely employed control strategy is related to the optimal control theory, considering external or without constraints.…”

Section: Introductionmentioning

confidence: 99%

Chaos Suppression via Euler-Lagrange Control Design for a Class of Chemical Reacting System

Aguilar-López

2018

Mathematical Problems in Engineering

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In this work the problem of chaos suppression for a class of continuous chemical reactor with chaotic dynamics is tackled via a nonlinear control strategy. The proposed controller is developed under the framework of optimal control theory, where a functional is proposed to maximize the chemical reaction rate via a proposed Lagrangian-type which contains directly the state equation of the reacting system, avoiding the problems of Lagrange, the Hamiltonian formulation, and consequently the explicit constraints to the system. This allows solving in an easier form the optimization problem in comparison with the standard methods. This procedure allows suppressing the chaotic behavior of the reacting system by stabilizing the reaction rate term by leading it to an extreme value. Numerical experiments are done in order to show the satisfactory performance of the proposed methodology.

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Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System

Deepa

Jayalakshmi

2021

IETE Journal of Research

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Fuzzy modelling and model reference neural adaptive control of the concentration in a chemical reactor (CSTR)

Cited by 4 publications

References 9 publications

FOPID Neural Network Controller Design for Nonlinear CSTR System

FOPID Neural Network Controller Design for Nonlinear CSTR System

Chaos Suppression via Euler-Lagrange Control Design for a Class of Chemical Reacting System

Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System

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