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Control law reconfiguration for non linear systems based on multilayer neural network and fuzzy model: application to a thermal plant

机译:基于多层神经网络和模糊模型的非线性系统控制律重构:在火力发电厂中的应用

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In this paper, a reconfiguration approach using fuzzy logic algorithm and neural network modelling is proposed. When failure has been detected, the state of the degraded system is evaluated by comparing the output of the system with the estimation provided by a neural model. Therefore, we propose to use the neural network in order to cover all the operating zone of the faulty system. By combining neural network capabilities and fuzzy logic for fault evaluation, a new control law is determined taking into account the impact of the failure on the system. Its potentialities are illustrated through simulation studies on a thermal plant presenting bilinear characteristics.
机译:本文提出了一种利用模糊逻辑算法和神经网络建模的重新配置方法。当检测到失败时,通过将系统的输出与神经模型提供的估计进行比较来评估降级系统的状态。因此,我们建议使用神经网络以覆盖故障系统的所有操作区。通过组合神经网络能力和模糊逻辑进行故障评估,确定了一个新的控制法,以考虑到系统的失败的影响。通过呈现双线性特性的热植物的模拟研究来说明其潜力。

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