文章基本信息

标题：A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization ⁎
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作者：Farshud Sorourifar ; Georgios Makrygirgos ; Ali Mesbah 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：3
页码：243-250
DOI：10.1016/j.ifacol.2021.08.249
语种：English
出版社：Elsevier
摘要：AbstractThe closed-loop performance of model predictive controllers (MPCs) is highly dependent on the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy, instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating the tuning of MPC under uncertainty. In particular, we formulate the automated tuning problem as a constrained black-box optimization problem that can be tackled with derivative-free optimization. We rely on a constrained variant of Bayesian optimization to solve the MPC tuning problem that can directly handle noisy and expensive-to-evaluate functions. The benefits of the proposed automated tuning approach are demonstrated on a benchmark continuously stirred tank reactor case study.
关键词：KeywordsModel predictive controlConstrained Bayesian optimizationModel learning