A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization
نویسندگان
چکیده
Abstract The closed-loop performance of model predictive controllers (MPCs) is highly dependent on the choice prediction models, controller formulation, and tuning parameters. However, models are typically optimized for accuracy, instead performance, MPC done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach automating under uncertainty. particular, formulate automated problem as constrained black-box optimization that can be tackled with derivative-free optimization. We rely variant Bayesian solve directly handle noisy expensive-to-evaluate functions. benefits proposed demonstrated benchmark continuously stirred tank reactor case study.
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2021
ISSN: ['2405-8963', '2405-8971']
DOI: https://doi.org/10.1016/j.ifacol.2021.08.249