Multi-level Optimal Control with Neural Surrogate Models

• URL: http://arxiv.org/abs/2402.07763v1
• Date: Mon, 12 Feb 2024 16:28:57 GMT
• Title: Multi-level Optimal Control with Neural Surrogate Models
• Authors: Dante Kalise, Estefan\'ia Loayza-Romero, Kirsten A. Morris, Zhengang Zhong
• Abstract summary: The evaluation of the optimal closed loop for a given actuator realisation is a computationally demanding task. The use of neural network surrogates to replace the lower level of the optimisation hierarchy is proposed. The effectiveness of the proposed surrogate models and optimisation methods is assessed in a test related to optimal actuator location for heat control.
• Score: 0.7646713951724013
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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