Version 1
: Received: 23 May 2022 / Approved: 25 May 2022 / Online: 25 May 2022 (11:17:19 CEST)
How to cite:
Capel, M.I. Artificial Neuron-based Model for a Hybrid Real-Time System: Induction Motor Case Study. Preprints2022, 2022050352. https://doi.org/10.20944/preprints202205.0352.v1
Capel, M.I. Artificial Neuron-based Model for a Hybrid Real-Time System: Induction Motor Case Study. Preprints 2022, 2022050352. https://doi.org/10.20944/preprints202205.0352.v1
Capel, M.I. Artificial Neuron-based Model for a Hybrid Real-Time System: Induction Motor Case Study. Preprints2022, 2022050352. https://doi.org/10.20944/preprints202205.0352.v1
APA Style
Capel, M.I. (2022). Artificial Neuron-based Model for a Hybrid Real-Time System: Induction Motor Case Study. Preprints. https://doi.org/10.20944/preprints202205.0352.v1
Chicago/Turabian Style
Capel, M.I. 2022 "Artificial Neuron-based Model for a Hybrid Real-Time System: Induction Motor Case Study" Preprints. https://doi.org/10.20944/preprints202205.0352.v1
Abstract
A correct system design can be systematically obtained from a specification model of a real-time system that integrates hybrid measurements in a realistic industrial environment, this has been carried out through complete Matlab / Simulink / Stateflow models. However, there is a widespread interest in carrying out that modeling by resorting to Machine Learning models, which can be understood as Automated Machine Learning for Real-time systems that present some degree of hybridization. An induction motor controller which must be able to maintain a constant air flow through a filter is one of these systems and it is discussed in the paper as a study case of closed-loop control system. The article discusses a practical application of ML methods that demonstrates how to replace such closed loop in industrial control systems with a Simulink block generated from neural networks to show how the proposed procedure can be applied to derive complete hybrid system designs with artificial neural networks (ANN). In the proposed ANN-based method to design a real-time hybrid system with continuous and discrete components, we use a typical design of a neural network, in which we define the usual phases: training, validation, and testing. The generated output of the model is made up of reference variables values of the cyber-physical system, which represent the functional and dynamic aspects of model. They are used to feed Simulink/Stateflow blocks in the real target system.
Computer Science and Mathematics, Applied Mathematics
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.
