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Lyapunov-Based Dynamic Neural Network for Adaptive Control of Complex Systems

Journal of Software Engineering and Applications 2012年第4期5卷 225-248页

作者： Farouk Zouari Kamel Ben Saad Mohamed Benrejeb unité de recherche lara automatique Ecole Nationale d’Ingénieurs de Tunis (ENIT) Tunis Tunisia unité de recherche lara automatique Ecole Nationale d’Ingénieurs de Tunis (ENIT) Tunis Tunisia.

In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.

关键词： Complex Dynamical Systems Lyapunov Approach Recurrent Neural Networks Adaptive Control

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Adaptive Internal Model Control of a DC Motor Drive System Using Dynamic Neural Network

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Journal of Software Engineering and Applications 2012年第3期5卷 168-189页

作者： Farouk Zouari Kamel Ben Saad Mohamed Benrejeb unité de recherche lara automatique Ecole Nationale d'Ingénieurs de Tunis (ENIT) Tunis Tunisia.

This work concerns the study of problems relating to the adaptive internal model control of DC motor in both cases conventional and neural. The most important aspects of design building blocks of adaptive internal model control are the choice of architectures, learning algorithms, and examples of learning. The choice of parametric adaptation algorithm for updating elements of the conventional adaptive internal model control shows limitations. To overcome these limitations, we chose the architectures of neural networks deduced from the conventional models and the Levenberg-marquardt during the adjustment of system parameters of the adaptive neural internal model control. The results of this latest control showed compensation for disturbance, good trajectory tracking performance and system stability.

关键词： Adaptive Internal Model Control Recurrent Neural Network DC Motor Parametric Adaptation Algorithm Levenberg-Marquardt

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Stabilizing First-Order Controllers for TCP/AQM Networks

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Computer Technology and Application 2011年第12期2卷 979-984页

作者： Sana Testouri Karim Saadaoui Mohamed Benrejeb unite de recherche lara-automatique Ecole Nationale d＇Ingdnieurs de Tunis BP 37 le Belvdd~re 1002 Tunis Tunisie

In this paper, the authors investigate the overload problem of a congested router in TCP （Transmission Control Protocol） networks, with a design of an AQM （Active Queue Management） controller based on control theory. The model of TCP/AQM is converted into a second-order system with time delay, the stability of the closed-loop AQM system is described in terms of a characteristic quasi-polynomial. The authors focus on the synthesis of first-order AQM controller. The authors＇ approach consists of determining the stabilizing regions in the parameter space of the first-order controller, then choosing controller＇s parameters within these regions. Finally, the authors illustrate the proposed methodology with an example and simulations using NS-2 simulator.

关键词： Stability time delay network congestion control active queue management.

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A Comparative Study of Nonlinear Time-Varying Process Modeling Techniques: Application to Chemical Reactor

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Journal of Intelligent Learning Systems and Applications 2012年第1期4卷 20-28页

作者： Errachdi Ayachi Saad Ihsen Benrejeb Mohamed lara automatique Tunis Tunisia.

This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron (MLP). The MLP model consists of an input layer, an output layer and usually one or more hidden layers. However, training MLP network based on back propagation learning is computationally expensive. In this paper, an RBF network is called. The parameters of the RBF model are optimized by two methods: the Gradient descent (GD) method and Genetic Algorithms (GA). However, the MLP model is optimized by the Gradient descent method. The performance of both models are evaluated first by using a numerical simulation and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. It has been shown that in both validation operations the results were successful. The optimized RBF model by Genetic Algorithms gave the best results.

关键词： Nonlinear Systems Time-Varying Systems Multi Layer Perceptron Radial Basis Function Gradient descent Genetic Algorithms Optimization

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