Multi-level optimal control with neural surrogate models
File(s)
Preprint version
OA Location
Author(s)
Kalise, Dante
Loayza-Romero, Estefanía
Morris, Kirsten A
Zhong, Zhengang
Type
preprint
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.
Date Issued
2024-02-12
Citation
arXiv, 2024
Journal / Book Title
arXiv
Copyright Statement
Copyright © 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Description
Preprint version
Identifier
http://arxiv.org/abs/2402.07763v1
Subjects
math.OC
math.OC
cs.LG
cs.NA
math.NA