Adaptive Neural Network Architectures For Emission Control of Engines (TSE-03G)

用于发动机排放控制的自适应神经网络架构 (TSE-03G)

• 批准号: 0327877
• 负责人: Jagannathan Sarangapani
• 金额: --
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0327877&HistoricalAwards=false
• 关键词: Adaptive; Neural; Network; Architectures; Emission

The goal of this project is to provide the next generation emission controller for engines with adaptation, optimization, and learning so as to achieve low NOx and improved fuel economy. The controller will guarantee performance in the presence of unknown nonlinearities, feedback delays, and is supported by a rigorous design and mathematical framework. Specific objectives to support that goal are:1. Develop a robust adaptive NN control scheme for SI engines that would minimize the effect cyclic dispersion at very lean operation (equivalence ratio =0.7) by selecting appropriate feedback parameters.2. Study and model the complex dynamics in cyclic output for SI engines under the influence of high-levels of EGR. Investigate potential for reducing NOx over 50% below current EGR systems.3. Develop a robust adaptive critic NN EGR control scheme that would minimize the effect of cyclic dispersion and would allow satisfactory performance in low NOx regimes via appropriate feedback. Provide methodology to integrate combustion stability with high levels of EGR in SI engines.4. Simulate and verify the EGR and lean stability controller performance on an experimentally validated model. Demonstrate the controller schemes on a single cylinder engine in the laboratory.5. Investigate the complex dynamics in output for diesel engines under the influence of high levels of EGR with an objective of reducing the NOx over 50% with minimal particulate matter. Provide recommendations about the applicability of the proposed EGR controllers for diesel engines.These projects will be pursued in collaboration with organizations such as Caterpillar, Inc. and Oak Ridge National Laboratory. These areas of research could lead to significant advances in the development of advanced control schemes for non-strict feedback nonlinear systems such as next generation spark ignition, diesel as well as nontraditional engines such as homogeneous charge compression ignition, direct injection spark ignition and hybrid engines (electric power and gasoline).

该项目的目的是通过适应，优化和学习为发动机提供下一代排放控制器，以实现低NOX和改善的燃油经济性。控制器将在存在未知的非线性，反馈延迟的情况下保证性能，并由严格的设计和数学框架支持。支持该目标的具体目标是：1。为SI发动机开发强大的自适应NN控制方案，可以通过选择适当的反馈参数在非常精益的操作（等效比= 0.7）时最大程度地减少效应环状分散体。2。在EGR高水平的影响下，研究并模拟了SI发动机的循环输出的复杂动力学。调查将NOX降低到当前EGR系统的50％以上的潜力3。制定强大的自适应评论家NN EGR控制方案，该方案将最大程度地减少环状分散效果，并通过适当的反馈在低NOX制度中令人满意的性能。提供将燃烧稳定性与SI发动机中高水平EGR相结合的方法。4。在经过实验验证的模型上模拟和验证EGR和精益稳定性控制器的性能。在实验室中的单个气缸发动机上演示控制器方案5。研究柴油发动机在高水平EGR的影响下的输出动力学，目的是将NOX降低到50％以上，而最小的颗粒物。提供有关拟议的EGR控制器对柴油发动机的适用性的建议。这些项目将与Caterpillar，Inc。和Oak Ridge National Laboratory等组织合作进行。这些研究领域可能会导致非图案反馈非线性系统（例如下一代火花点火，柴油以及非传统发动机）的高级控制方案的开发，以及诸如均质电荷压缩点火，直接注射火花点火和混合发动机（电力和汽油）。