Smart Engines: Fuel Flexible Engine Control using Adaptive Neural Network Critics

智能发动机：使用自适应神经网络批评来实现灵活的发动机控制

• 批准号: 0901562
• 负责人: Jagannathan Sarangapani
• 金额: $ 33万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-15 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0901562&HistoricalAwards=false
• 关键词: Smart; Engines; Fuel; Flexible; Engine

AbstractLow temperature combustion engines such as homogeneous charge compression ignition (HCCI) offer fuel flexibility with high fuel efficiency and low emissions. If the type and composition of the fuel, such as bio-fuel, is not known a-priori, a ?smart? engine has to be capable of sensing heat release and adjusting combustion system parameters online for minimized emissions and fuel consumption. This necessitates a more advanced adaptive control schemes for the control of these types of complex non-affine nonlinear systemsThe overall goal of this study is to provide the next generation adaptive critic neural net controllers for complex non-affine, nonlinear systems supported by a rigorous and repeatable design and mathematical framework. The controller performance will be validated for the HCCI engine for a range of bio-mass based fuel stocks using conventional and novel input sensors for measuring cyclic heat release. Intellectual Merit: The project will advance the state of the art in Adaptive Dynamic Programming for control by providing rigorous mathematical analysis for convergence and stability, and performance guarantees in the presence of approximation errors, actuator constraints and delays. Moreover, by applying the theoretical results to an emerging control application of fuel-flexible engines, this type of controllers will be implemented and tested in hardware in the Co-PI's internal combustion engine laboratory.Broader Impact: Improved control of next generation fuel-flexible engines and multi mode engines, such as plug-in hybrids, is expected to improve fuel efficiency and reduce harmful emission, thus directly impacting the environment and reducing dependence on foreign oil. Research results will be integrated as part of undergraduate course and laboratories. Dissemination plans include distribution of software through websites, patents, journal and conference publications. The PIs have a track record of hiring underrepresented minorities through MST?s Minority Engineering Program, extending research opportunities to undergraduates via REU supplements and interactions with EPSCOR states. International collaborations will be pursued. Technology transfer to industrial members is planned through the NSF I/UCRC Site on Intelligent Maintenance Systems where the PI is the Site Director.

摘要均质充气压缩点火(HCCI)等低温内燃机具有燃油灵活性高、燃油效率高、排放低等优点。如果燃料的类型和组成，如生物燃料，事先是未知的，那么智能？发动机必须能够感知热释放并在线调整燃烧系统参数，以将排放和燃油消耗降至最低。这就需要一种更先进的自适应控制方案来控制这类复杂的非仿射非线性系统。本研究的总体目标是为复杂的非仿射非线性系统提供新一代自适应批判神经网络控制器，并提供严格和可重复的设计和数学框架。对于一系列基于生物质量的燃料库存，将使用传统和新型输入传感器测量循环热释放，对HCCI发动机的控制器性能进行验证。智能优点：该项目将通过为收敛和稳定性提供严格的数学分析，并在存在逼近误差、执行器限制和延迟的情况下提供性能保证，从而推动自适应动态编程控制领域的最先进水平。此外，通过将理论结果应用于燃料柔性发动机的新兴控制应用，这种类型的控制器将在Co-PI的内燃机实验室进行硬件实施和测试。广泛影响：改进对下一代燃料柔性发动机和多模式发动机(如插电式混合动力车)的控制，有望提高燃油效率，减少有害排放，从而直接影响环境，减少对外国石油的依赖。研究成果将被整合为本科课程和实验室的一部分。传播计划包括通过网站、专利、期刊和会议出版物分发软件。PIs有通过MST-S少数民族工程计划雇用代表不足的少数民族的记录，通过REU补充和与EPSCoR州的互动向本科生提供研究机会。将寻求国际合作。对工业成员的技术转让计划通过NSF I/UCRC关于智能维护系统的站点进行，其中PI是站点主任。