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Tuning of model predictive engine controllers over transient drive cycles ?

机译:在瞬态驱动周期中调整模型预测发动机控制器

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摘要

A framework for tuning the parameters of model predictive controllers (MPCs) based on gradient-free optimisation (GFO) is proposed. Efficient calibration of MPCs is often a difficult task given the large number of tuning parameters and their non-intuitive correlation with the output response. We propose an efficient and systematic framework for the tuning of MPC parameters that can be implemented iteratively within the closed-loop setting. The performance of the proposed GFO-based algorithm is evaluated through its application to air-path control for diesel engines over simulations and experiments. We illustrate that the tuned parameters provide satisfactory tracking of reference trajectories over engine drive cycles with only a few iterations. Thereby, we extend existing MPC tuning approaches that calibrate parameters using step responses on the fuel rate and engine speed onto tuning over a full drive cycle response.
机译:提出了一种基于无梯度优化(GFO)的模型预测控制器(MPC)参数的框架。考虑到具有大量调谐参数及其与输出响应的非直观相关性,MPC的高效校准通常是一项艰巨的任务。我们提出了一种高效且系统的框架,用于调谐MPC参数,该MPC参数可以在闭环设置内迭代地实现。通过其应用于柴油发动机通过模拟和实验的空路控制来评估所提出的基于GFO的算法的性能。我们说明了调谐参数在发动机驱动周期上提供了令人满意的跟踪,只有几个迭代。因此,我们扩展了使用步骤响应对燃料速率和发动机速度的现有MPC调谐方法,这些方法使用燃料速率和发动机速度在整个驱动周期响应上进行调整。

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