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首页> 外文期刊>SAE International Journal of Engines >Application of Reference Governor Using Soft Constraints and Steepest Descent Method to Diesel Engine Aftertreatment Temperature Control
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Application of Reference Governor Using Soft Constraints and Steepest Descent Method to Diesel Engine Aftertreatment Temperature Control

机译:软约束和最速下降法参考调速器在柴油机后处理温度控制中的应用

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摘要

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers. In order to realize the online RG algorithm, the following three elements are needed: (i) a plant model to predict future behavior of the control system, (ii) an objective function that quantifies how suitable a modified reference candidate is, and (iii) an online optimization algorithm that computes the most suitable modified reference from a set of candidates. For (i), a catalyst temperature model is derived based on thermal exchanges. In regards to (ii), three objective function candidates are considered and, through simulations, one in which a constraint is characterized as a soft constraint by a barrier function that penalizes the constraint violation is chosen. Owing to the parameters in the objective function selected, the resulting transient responses of the catalyst temperature can be tuned. (iii) The optimization algorithm is realized in the RG based on the steepest descent method, which minimizes a nonlinear objective function iteratively online. Finally, an experimental result is shown in which the present RG algorithm is applied to a production vehicle controller. The result shows the applicability and effectiveness of the present method.
机译:本文考虑了参考调速器(RG)在汽车柴油后处理温度控制中的应用。近来,关于车辆排放的法规变得更加严格,并且发动机硬件和软件预计将更加复杂。确保控制系统的约束以及良好的控制性能变得越来越困难。在可以直接处理约束的基于模型的控制方法中,本文重点介绍了RG,RG作为一种基于模型预测的控制方法最近引起了很多关注。在RG中,基于将来的预测修改跟踪控制中的参考,以使闭环系统中的预测输出满足约束条件。考虑到发动机嵌入式控制器的实时实现,本文提出了一种在线RG算法。为了实现在线RG算法,需要以下三个元素:(i)用于预测控制系统未来行为的工厂模型;(ii)量化修改后的参考候选对象是否合适的目标函数;以及(iii) )在线优化算法,该算法从一组候选项中计算出最合适的修改后的参考。对于(i),基于热交换推导催化剂温度模型。关于(ii),考虑了三个目标函数候选,并且通过模拟,选择了一种约束,该约束的特征是通过惩罚约束违反的障碍函数将约束描述为软约束。由于所选目标函数中的参数,可以调节催化剂温度的最终瞬态响应。 (iii)优化算法是基于最速下降法在RG中实现的,该算法可在线迭代地最小化非线性目标函数。最后,示出了将本发明的RG算法应用于生产车辆控制器的实验结果。结果表明了该方法的适用性和有效性。

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