文章基本信息

标题：Improved Directional Derivatives for Modifier-Adaptation Schemes
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作者：Martand Singhal ; Alejandro G. Marchetti ; Timm Faulwasser 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：5718-5723
DOI：10.1016/j.ifacol.2017.08.1124
语种：English
出版社：Elsevier
摘要：AbstractModifier adaptation enables the real-time optimization (RTO) of plant operation in the presence of considerable plant-model mismatch. For this, modifier adaptation requires the estimation of plant gradients, which is experimentally expensive as this might involves several online experiments. Recently, adirectionalmodifier-adaptation approach has been proposed, which uses the process model to compute offline a subset of input directions that are critical for plant optimization. This allows estimating directional derivatives only in the critical directions instead of full gradients, thereby reducing the burden of gradient estimation. However, in certain cases such as change of active constraints and large parametric uncertainties, directional modifier adaptation may lead to significant suboptimality. Here, we propose an extension to directional modifier adaptation, whereby, at each RTO iteration, we compute a set of critical directions that are robust to large parametric perturbations. We draw upon a simulation study of the run-to-run optimization of the Williams-Otto semi-batch reactor to illustrate the performance of the proposed extension.
关键词：Keywordsreal-time optimizationmodifier adaptationdirectional gradient estimation