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Adaptive Internal Model Control of a DC Motor Drive System Using Dynamic Neural Network
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作者 farouk zouari Kamel Ben Saad Mohamed Benrejeb 《Journal of Software Engineering and Applications》 2012年第3期168-189,共22页
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 mod... 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. 展开更多
关键词 Adaptive Internal Model Control RECURRENT NEURAL Network DC MOTOR PARAMETRIC ADAPTATION Algorithm LEVENBERG-MARQUARDT
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Lyapunov-Based Dynamic Neural Network for Adaptive Control of Complex Systems
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作者 farouk zouari Kamel Ben Saad Mohamed Benrejeb 《Journal of Software Engineering and Applications》 2012年第4期225-248,共24页
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-o... 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. 展开更多
关键词 Complex DYNAMICAL Systems LYAPUNOV Approach RECURRENT NEURAL Networks Adaptive Control
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