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

该项目的目标是为发动机提供具有自适应、优化和学习能力的下一代排放控制器，以实现低NOx和提高燃油经济性。该控制器将在存在未知非线性、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。支持这一目标的具体目标是：通过选择适当的反馈参数，为SI引擎开发一个鲁棒的自适应神经网络控制方案，该方案将在非常精益的操作（等效比=0.7）下最小化循环分散的影响。研究并建立了高水平EGR影响下SI发动机循环输出的复杂动力学模型。2 .研究在现有EGR系统基础上减少NOx 50%以上的潜力。开发一种鲁棒的自适应批评神经网络EGR控制方案，将循环分散的影响降至最低，并通过适当的反馈在低氮氧化物状态下实现令人满意的性能。提供方法来整合燃烧稳定性与高水平的EGR在SI发动机。在实验验证的模型上仿真并验证了EGR和精益稳定控制器的性能。在实验室的单缸发动机上演示控制器方案。研究柴油发动机在高水平EGR影响下的复杂动态输出，目标是在最小颗粒物的情况下将NOx减少50%以上。就建议的EGR控制器在柴油发动机上的适用性提出建议。这些项目将与卡特彼勒公司和橡树岭国家实验室等组织合作进行。这些领域的研究可能会导致非严格反馈非线性系统的先进控制方案的发展取得重大进展，如下一代火花点火，柴油以及非传统发动机，如均匀电荷压缩点火，直接喷射火花点火和混合动力发动机（电力和汽油）。

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).