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Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization
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DOI：10.1016/j.jprocont.2022.11.006.png)
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Extremum seeking controllers have been investigated for multivariable data-driven energy optimization in heat pumps. In particular, proportional-integral extremum seeking control (PI-ESC) has demonstrated potential for significant acceleration compared to other ESC variants for nonlinear closed-loop control systems. A barrier to PI-ESC's utilization in self-optimizing control is the fact that the PI-ESC algorithm is fragile. That is, unless the PI-ESC gains (e.g., controller gains, estimator gains) are carefully tuned, small perturbations to these gains can render the closed-loop unstable. Since arbitrary combinations of PI-ESC gains can result in instabilities, we propose a failure-robust Bayesian optimization (FRBO) algorithm that computes PI-ESC gains that ensure the closed-loop system can be driven rapidly to the optimum, while identifying and avoiding regions in the space of PI-ESC gains that are likely to result in instabilities (i.e., failures). The FRBO-tuned PI-ESC is shown to result in rapid closed-loop convergence to optimal values both on benchmark examples and a production-level model of an air conditioning system. (c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Multivariable control
Self-optimizing control
Gaussian processes
Bayesian optimization
Extremum seeking
Vapor compression system
Refrigerant cycle
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3.9
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被引数:
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