文章基本信息

标题：Tuning of model predictive engine controllers over transient drive cycles ⁎
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作者：Alejandro I. Maass ; Chris Manzie ; Iman Shames 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14022-14027
DOI：10.1016/j.ifacol.2020.12.923
语种：English
出版社：Elsevier
摘要：AbstractA 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.
关键词：KeywordsGradient-free optimisationmodel-based controlcontroller calibrationdiesel enginesautomotive control