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A neuro-fuzzy controller for a stoker-fired boiler, based on behavior modeling

机译:基于行为建模的用于煤烟锅炉的神经模糊控制器

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摘要

A key issue an industrial stoker-fired boiler is the design of an efficient and robust controller for its combustion system, so that the boiler can provide a continuous supply of steam at the desired pressure conditions. However, it is difficult to achieve this objective by using a model-based approach because of the high nonlinearity and uncertainty of boiler systems. In addition, the control performance may also suffer as a result of strong load changes, large disturbances, large time lags, and so forth. This paper presents a behavior-modeling-based approach to the design of a neuro-fuzzy controller for the combustion control of a stoker-fired boiler. In this approach, boiler combustion processes with unknown structure are modeled by defining three dynamic behaviors. According to these behavior `templates', their corresponding fuzzy-logic controllers can be optimized off-line. During boiler system operation, the appropriate fuzzy-logic controller is fired, based on an on-line assessment of its dynamic behavior. The application results obtained demonstrate the effectiveness and the robustness of the proposed controller.
机译:工业燃煤锅炉的关键问题是为其燃烧系统设计高效且坚固的控制器,以便锅炉可以在所需压力条件下提供连续的蒸汽供应。然而,由于锅炉系统的高度非线性和不确定性,使用基于模型的方法很难实现这一目标。另外,由于强烈的负载变化,较大的干扰,较大的时滞等,控制性能也会受到影响。本文提出了一种基于行为模型的方法来设计用于烟囱锅炉燃烧控制的神经模糊控制器。在这种方法中,通过定义三个动态行为来模拟结构未知的锅炉燃烧过程。根据这些行为“模板”,可以离线优化其相应的模糊逻辑控制器。在锅炉系统运行期间,基于对其动态行为的在线评估,将触发适当的模糊逻辑控制器。获得的应用结果证明了所提出控制器的有效性和鲁棒性。

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