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Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization.
- Source :
- IEEE Transactions on Industrial Electronics; Feb2023, Vol. 70 Issue 2, p2128-2138, 11p
- Publication Year :
- 2023
-
Abstract
- This article presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian processes. The safety requirement is imposed via a barrier-like term in the objective, which is introduced to account for operational changes in the system. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method through simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02780046
- Volume :
- 70
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Industrial Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 160651984
- Full Text :
- https://doi.org/10.1109/TIE.2022.3158007