An internal model control strategy using artificial neural networks for a class of nonlinear systems

Ali, Saloua Bel Hadj; Abed-Abdelkrim, A. El; Benrejeb, Mohamed

doi:10.1109/icsmc.2002.1176373

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…2 shows the IMC diagram with the filter. Here, the controller is C P(s)= 1-MC (8) 5(s) = MC (9) Through the above IMC parameterization, the controller C is related to p(s) and v(s) in a very simple linear manner which make the design of C easy. The effect of the classical controller on sensitivity function p(s) and complimentary sensitivity function v(s) is more complex.…”

Section: Internal Model Controlmentioning

confidence: 99%

Design and Real-Time Implementation of an Internal Model Speed Control for an Induction Motor

Mohamed

Yang

Moghavvemi

2008

2008 IEEE Conference on Robotics, Automation and Mechatronics

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This paper carries out the design and real time implementation of an Internal Model Controller (IMC) to control the speed of an induction motor and precisely a squirrel cage type. The scheme in this paper is constructed using a model and a controller both being Artificial Neural Network (ANN) based. This ANN-based control scheme has been chosen because of its ability to handle the strong nonlinearities of the induction motor. The performance of the controller is tested by applying different types of input signals as well as load torque disturbances. Whether loaded or unloaded, the proposed internal model controller has proved to achieve high performance and accuracy.

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Section: Internal Model Controlmentioning

confidence: 99%

Design and Real-Time Implementation of an Internal Model Speed Control for an Induction Motor

Mohamed

Yang

Moghavvemi

2008

2008 IEEE Conference on Robotics, Automation and Mechatronics

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“…The difference between the outputs from the plant and the forward model is computed as an error signal, which is subtracted from the set point before the introduction to the controller. Because of the mismatch handling feature, this strategy is able to force the controller to be close to the desired set points 51. The structure of the IMC strategy is as presented in Fig.…”

Section: Debutanizer Columnmentioning

confidence: 99%

Intelligent Control of an Industrial Debutanizer Column

et al. 2022

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A debutanizer column located at a refinery in Malaysia produces LPG as its top product and light naphtha as its bottom product. Control of the product compositions of the column is very important to maintain the desired purities. The current control design of the debutanizer column is based on the classical proportional integral derivative approach, which has been found to be less effective in controlling the variations in the column as the process is characterized by strong nonlinear and uncertain dynamics. Here, intelligent control based on an adaptive neurofuzzy inference system (ANFIS) is investigated and compared against an artificial neural network, for the special case of limited training data of the industrial debutanizer column. The ANFIS-based controller outperforms the other control configurations for both set point tracking and disturbance rejection cases and has better generalization performance even when trained with limited data samples.

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“…The first approach uses a neuronal model and a neural controller and will be the subject of this paragraph and the second one uses a fuzzy controller. The aim of this part is to characterize an internal model control structure, using the artificial neural networks according to Figure , . The neural model of the handwriting process is connected in parallel with the process.…”

Section: To a Global Model Designmentioning

confidence: 99%

Conventional and non conventional body motions modelling and control. Application to the handwriting process

Abdelkrim

Benrejeb

2019

Asian Journal of Control

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The handwriting process is one of the most important and complex activities of the human body. In recent years, modelling and analyzing and controlling it have received great attention leading to large important motion studies results. This paper attempts to present an overview of recent works about our proposed approaches and other ones to model and control this process. Hence, an overall model of the handwriting process has been proposed and developed by the coauthors, showing the interest of using the soft computing concepts, namely neural networks and fuzzy logic. The design of this model requires several undertaken steps. Starting from an experimental approach to acquire electromyographic signals and writing positions, different conventional and non conventional models have been proposed. Besides some control strategies have been considered in particular neural and fuzzy approaches in addition to non conventional neural and neuro-fuzzy Internal Model Control strategy. The proposed global model, inspired from the biological structure system, has been designed and validated through simulation results.

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An internal model control strategy using artificial neural networks for a class of nonlinear systems

Cited by 7 publications

References 5 publications

Design and Real-Time Implementation of an Internal Model Speed Control for an Induction Motor

Design and Real-Time Implementation of an Internal Model Speed Control for an Induction Motor

Intelligent Control of an Industrial Debutanizer Column

Conventional and non conventional body motions modelling and control. Application to the handwriting process

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