Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty
Author:
Affiliation:
1. Department of Chemical and Biomolecular Engineering The Ohio State University Columbus Ohio USA
2. Department of Chemical and Biomolecular Engineering University of California Berkeley California USA
Funder
National Aeronautics and Space Administration
National Science Foundation of Sri Lanka
Publisher
Wiley
Subject
General Chemical Engineering,Environmental Engineering,Biotechnology
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/aic.17591
Reference49 articles.
1. PaulsonJA MesbahA.Shaping the closed‐loop behavior of nonlinear systems under probabilistic uncertainty using arbitrary polynomial chaos. Proceedings of the IEEE Conference on Decision and Control; 2018: 6307‐6313.
2. Model Predictive Control Tuning Methods: A Review
3. A model-free approach for auto-tuning of model predictive control
4. Data-Efficient Autotuning With Bayesian Optimization: An Industrial Control Study
5. Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees
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