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Intelligent Control of a Battery Charging Process in a Real Plant

机译:实际工厂中电池充电过程的智能控制

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摘要

We describe in this paper, different hybrid approaches for controlling dynamical systems in electrochemical applications. The hybrid approaches combine soft computing techniques and mathematical models to achieve the goal of controlling the electrochemical process to follow a desired production plan. We have developed several hybrid architectures that combine fuzzy logic, neural networks, and genetic algorithms, to compare the performance of each of these combinations and decide on the best one for our purpose. Electrochemical processes, like the ones used in battery charging, are very complex and for this reason very difficult to control. We have achieved very good results using fuzzy logic for control, neural networks for modelling the process, and genetic algorithms for tuning the hybrid intelligent system.
机译:我们在本文中描述了控制电化学应用中动力系统的不同混合方法。混合方法结合了软计算技术和数学模型,以达到控制电化学过程以遵循所需生产计划的目的。我们已经开发了几种混合结构,这些结构结合了模糊逻辑,神经网络和遗传算法,以比较每种组合的性能,并为我们的目的选择最佳组合。像电池充电过程一样,电化学过程非常复杂,因此很难控制。我们使用模糊逻辑进行控制,使用神经网络对过程进行建模以及使用遗传算法对混合智能系统进行调整,已经取得了非常好的结果。

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