Multi-level optimal control with neural surrogate models

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Kalise, Dante and Loayza Romero, Estefania and Zhong, Zhengang and Morris, Kirsten A. (2024) Multi-level optimal control with neural surrogate models. IFAC-PapersOnLine, 58 (17). pp. 292-297. ISSN 2405-8963 (https://doi.org/10.1016/j.ifacol.2024.10.184)

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Abstract

Optimal actuator and control design is studied as a multi-level optimisation problem, where the actuator design is evaluated based on the performance of the associated optimal closed loop. The evaluation of the optimal closed loop for a given actuator realisation is a computationally demanding task, for which the use of a neural network surrogate is proposed. The use of neural network surrogates to replace the lower level of the optimisation hierarchy enables the use of fast gradient-based and gradient-free consensus-based optimisation methods to determine the optimal actuator design. The effectiveness of the proposed surrogate models and optimisation methods is assessed in a test related to optimal actuator location for heat control.

ORCID iDs

Kalise, Dante, Loayza Romero, Estefania ORCID logoORCID: https://orcid.org/0000-0001-7919-9259, Zhong, Zhengang and Morris, Kirsten A.;
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