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Online Learning of a Neural Fuel Control System for Gaseous Fueled SI Engines.

机译：在线学习气态SI发动机的神经燃料控制系统。

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摘要

This dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels such as gasoline, both at the tailpipe and on a total cycle basis. Unfortunately, it can be expensive to convert vehicles to gaseous fuels, partially due to small production runs for these vehicles. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase.;The dynamic model of the system is concerned with the significant transport delay between the time the fuel is injected and when the exhaust gas oxygen sensor makes the reading. One significant result of this research is the realization that a previous commonly used model for this delay has become significantly less accurate due to the shift from carburettors or central point injection to port injection.;In addition to a description of the control scheme used, this dissertation includes a new method of algebraically inverting a neural network, avoiding computationally expensive iterative methods of optimizing the model. This can greatly speed up the control loop (or allow for less expensive, slower hardware).;An important feature of a fuel control scheme is that it produces a small, stable limit cycle between rich and lean fuel-air mixtures. This dissertation expands the currently available models for the limit cycle characteristics of a system with a linear controller as well as developing a similar model for the neural network controller by linearizing the learning scheme.;The controller is based upon a two-part model, separating steady state and dynamic effects. This model is then used to estimate the optimum fuelling for the measured operating condition. The steady state model is calculated using an artificial neural network with an online learning scheme, allowing the model to continually update to improve the controller's performance. This is important during both the initial learning of the characteristics of a new engine, as well as tracking changes due to wear or damage.;One of the most important aspects of this research is an experimental test, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and the controller required no calibration and very little information about the properties of the engine.;The significant original contributions resulting from this research include: (1) collection and summarization of previous work, (2) development of a method of automatically determining the pure time delay between the fuel injection event and the feedback measurement, (3) development of a more accurate model for the variability of the transport delay in modern port injection engines, (4) developing a fuel-air controller requiring minimal knowledge of the engine's parameters, (5) development of a method of algebraically inverting a neural network which is much faster than previous iterative methods, (6) demonstrating how to initialize the neural model by taking advantage of some important characteristics of the system, (7) expansion of the models available for the limit cycle produced by a system with a binary sensor and delay to include integral controllers with asymmetrical gains, (8) development of a limit cycle model for the new neural controller, and (9) experimental verification of the controller's tailpipe emissions performance, which compares favourably to the OEM controller.
机译：本文提出了一种新型的气体燃料汽车燃料控制算法。事实证明，无论是在尾管还是在整个循环的基础上，诸如氢和天然气之类的气体燃料都比诸如汽油之类的液体燃料污染更少。不幸的是，部分地由于这些车辆的小批量生产，将车辆转换为气态燃料可能是昂贵的。新车的主要开发成本之一是燃料控制器的开发和校准。此处介绍的研究包括不需要昂贵的校准阶段的燃料控制器。系统的动态模型与燃料喷射时间到废气氧传感器读数之间的显着运输延迟有关。这项研究的一个重要结果是认识到，由于从化油器或中心点喷射转变为进气道喷射，该延迟的先前常用模型的准确性已大大降低；除了对所用控制方案的描述之外，论文包括一种代数反转神经网络的新方法，避免了优化模型所需的计算量大的迭代方法。这可以大大加快控制回路的速度（或允许使用更便宜，更慢的硬件）。燃油控制方案的一个重要特征是，它在浓混合气和稀混合气之间产生一个小的，稳定的极限循环。本文扩展了线性控制器系统极限循环特性的现有模型，并通过线性化学习方案为神经网络控制器开发了类似的模型。控制器基于两部分模型，分离稳态和动态影响。然后，使用该模型来估算所测运行条件下的最佳加油量。稳态模型是使用带有在线学习方案的人工神经网络计算的，允许模型不断更新以提高控制器的性能。这在新发动机特性的初始学习以及跟踪由于磨损或损坏而引起的变化方面都非常重要。该研究最重要的方面之一是实验测试，其中控制器安装在发动机上。用天然气为燃料的卡车。装有新控制器的卡车的尾气排放在一氧化碳和氮氧化物上均显示出比OEM控制器更好的结果，并且该控制器无需校准，并且几乎不需要有关发动机性能的信息。研究包括：（1）收集和总结以前的工作，（2）开发一种自动确定燃油喷射事件和反馈测量之间的纯时间延迟的方法，（3）开发一个更准确的模型用于现代港口喷射发动机的运输延误，（4）开发对空气发动机控制器要求最少的发动机参数知识，（5）开发代数反转神经网络的方法，该方法比以前的迭代方法快得多，（（6 ）展示了如何利用系统的一些重要特征来初始化神经模型，（7）展开f对于具有二进制传感器和延迟的系统所产生的极限循环可用模型，包括具有非对称增益的积分控制器，（8）为新的神经控制器开发极限循环模型，以及（9）控制器的实验验证排气管排放性能，可与OEM控制器相比。

著录项

  • 作者

    Wiens, Travis Kent.;

  • 作者单位

    The University of Saskatchewan (Canada).;

  • 授予单位 The University of Saskatchewan (Canada).;
  • 学科 Engineering Automotive.;Engineering Mechanical.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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