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Authors: Saber Yaghoobi and M. Sami Fadali

Affiliation: Department of Electrical and Biomedical Engineering, University of Nevada, Reno, U.S.A.

Keyword(s): Bouc-Wen Hysteresis Model, Constraint Satisfaction Problem, Control System Design, MBH Optimization Algorithm, Deep Learning, PID Controller.

Abstract: This paper proposes a new approach to control system design through solving a Constraint Satisfaction Problem (CSP) using artificial intelligence, first using a genetic algorithm then using a Convolutional Neural Network (CNN). The genetic algorithm determines the feasible controller parameters by minimizing a cost function subject to inequality design constraints. The CNN-finds the parameters by designing a deep neural network. It is shown that the evolutionary optimization algorithm converges almost surely to the optimal solution. To demonstrate the methodologies, they are applied to the design of PID controllers for linear and nonlinear systems. Two examples are presented, an armature-controlled DC motor and Bouc-Wen nonlinear hysteresis model. Simulations results show that the proposed methods yield solutions that satisfy design specifications.

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Paper citation in several formats:
Yaghoobi, S. and Fadali, M. S. (2021). Control System Design via Constraint Satisfaction using Convolutional Neural Networks and Black Hole Optimization. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-522-7; ISSN 2184-2809, SciTePress, pages 232-239. DOI: 10.5220/0010618902320239

@conference{icinco21,
author={Saber Yaghoobi and M. Sami Fadali},
title={Control System Design via Constraint Satisfaction using Convolutional Neural Networks and Black Hole Optimization},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2021},
pages={232-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010618902320239},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Control System Design via Constraint Satisfaction using Convolutional Neural Networks and Black Hole Optimization
SN - 978-989-758-522-7
IS - 2184-2809
AU - Yaghoobi, S.
AU - Fadali, M.
PY - 2021
SP - 232
EP - 239
DO - 10.5220/0010618902320239
PB - SciTePress

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