Version 1
: Received: 22 April 2024 / Approved: 22 April 2024 / Online: 23 April 2024 (03:17:19 CEST)
How to cite:
Mînzu, V.; Arama, I.; Rusu, E. Machine Learning Algorithms that Emulate Controllers based on Particle Swarm Optimization - An Application to a Photobioreactor for Algae Growth. Preprints2024, 2024041409. https://doi.org/10.20944/preprints202404.1409.v1
Mînzu, V.; Arama, I.; Rusu, E. Machine Learning Algorithms that Emulate Controllers based on Particle Swarm Optimization - An Application to a Photobioreactor for Algae Growth. Preprints 2024, 2024041409. https://doi.org/10.20944/preprints202404.1409.v1
Mînzu, V.; Arama, I.; Rusu, E. Machine Learning Algorithms that Emulate Controllers based on Particle Swarm Optimization - An Application to a Photobioreactor for Algae Growth. Preprints2024, 2024041409. https://doi.org/10.20944/preprints202404.1409.v1
APA Style
Mînzu, V., Arama, I., & Rusu, E. (2024). Machine Learning Algorithms that Emulate Controllers based on Particle Swarm Optimization - An Application to a Photobioreactor for Algae Growth. Preprints. https://doi.org/10.20944/preprints202404.1409.v1
Chicago/Turabian Style
Mînzu, V., Iulian Arama and Eugen Rusu. 2024 "Machine Learning Algorithms that Emulate Controllers based on Particle Swarm Optimization - An Application to a Photobioreactor for Algae Growth" Preprints. https://doi.org/10.20944/preprints202404.1409.v1
Abstract
Some optimal control problems need metaheuristics in searching for the optimal solution, especially when the process has certain characteristics like the Photobioreactor for Algae Growth (distributed parameter, nonlinearities, etc.). Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations, there is a big computational effort that is reflected in a large controller execution time. The authors propose to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO, PM); the training data is obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple Linear Regression and the Regression Neural Networks were considered as predicting models. The paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models, implementing the controllers, and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior such that the process evolves almost identically. The resulting controllers’ execution time drastically decreased while keeping their optimality.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
