On Modeling and Nonlinear Model Reduction in Automotive Systems
On Modeling and Nonlinear Model Reduction in Automotive Systems
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发表时间：
2009
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通讯作者：
Oskar Nilsson
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作者：
Oskar Nilsson
汽车工业和其他地方的当前控制设计开发过程涉及许多昂贵的实验和控制参数的手动调整。基于模型的控制设计是一种很有前途的方法，以减少成本和开发时间。在这个过程中，低复杂度的模型是必不可少的，模型简化方法是非常有用的工具。本论文将建模与模型降阶的研究领域与汽车系统中的应用相结合。以发动机气路为例进行了模型降阶研究，并将发动机动力学建模时常用的启发式方法与基于平衡截断法的更系统的方法进行了比较。本文的主要贡献是提出了一种非线性系统的模型降阶方法。该程序的重点是减少使用的轨迹线性化获得的信息的状态的数量。该方法是紧密联系在一起的现有理论的误差范围和良好的结果显示在形式的例子，如在现实世界中使用的控制器汽车。此外，排气氧传感器，用于空燃比控制在汽车火花点火发动机的模型，开发和成功地验证。（减）
The current control design development process in automotive industry and elsewhere involves many expensive experiments and hand-tuning of control parameters. Model based control design is a promising approach to reduce costs and development time. In this process low complexity models are essential and model reduction methods are very useful tools. This thesis combines the areas of modeling and model reduction with applications in automotive systems. A model reduction case study is per- formed on an engine air path. The heuristic method commonly used when modeling engine dynamics is compared with a more systematic approach based on the balanced truncation method. The main contribution of this thesis is a method for model reduction of nonlinear systems. The procedure is focused on reducing the number of states using information obtained by linearization around trajectories. The methodology is closely tied to existing theory on error bounds and good results are shown in form of examples such as a controller used in real-world cars. Also, a model of the exhaust gas oxygen sensor, used for air-fuel ratio control in automotive spark-ignition engines, is developed and successfully validated. (Less)