Neural Network Controller Development and Implementation for Spark Ignition Engines With High EGR Levels
Neural Network Controller Development and Implementation for Spark Ignition Engines With High EGR Levels
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高 EGR 水平火花点火发动机的神经网络控制器开发与实现
DOI：
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发表时间：
2007
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通讯作者：
J. Drallmeier
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文献类型：
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作者：
J. Vance;Atmika Singh;B. Kaul;S. Jagannathan;J. Drallmeier
过去的研究表明，通过在火花点火（SI）发动机中使用10% -25%的废气再循环（EGR），可以大幅降低氮氧化物（NOx）浓度（见Dudek和Sain， 1989）。然而，在高EGR水平下，发动机在热释放中表现出强烈的循环分散，这可能导致发动机性能不稳定和不理想，从而使商用发动机无法在高EGR水平下运行。利用燃油作为控制输入，设计了一种基于神经网络（NN）的输出反馈控制器，以减少高EGR工况下发动机动态未知时热释放的循环变化。设计了一个单独的控制回路来控制EGR水平。给出了闭环系统的稳定性分析，并通过松弛分离原理、激励条件的持久性、确定性等价原理和未知参数假设的线性性证明了控制输入的有界性。自适应神经网络采用在线训练，不需要离线训练阶段。这种在线学习功能和无模型方法用于以最小的努力演示控制器在不同引擎上的适用性。仿真结果表明，该控制器在发动机模型上的应用显著降低了循环频散，并得到了实验验证。对于装有现代四气门机头的单缸发动机（里卡多发动机），在15% EGR下的实验结果表明，控制器使循环弥散减少了33%，燃油效率提高了2%，并且在没有EGR的化学计量操作中观察到氮氧化物下降了90%。此外，由于神经网络控制，未燃烧的碳氢化合物（uHC）比未控制的情况下降了6%，这是由于循环分散的下降。在不同的引擎上观察到类似的性能。
Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NOx from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine.