Control System Design via Constraint Satisfaction using Convolutional Neural Networks and Black Hole Optimization

Saber Yaghoobi, M. Fadali

2021

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.

Paper Citation

in Harvard Style

Yaghoobi S. and Fadali M. (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 - Volume 1: ICINCO, ISBN 978-989-758-522-7, pages 232-239. DOI: 10.5220/0010618902320239

in Bibtex Style

@conference{icinco21,
author={Saber Yaghoobi and M. 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 - Volume 1: ICINCO,},
year={2021},
pages={232-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010618902320239},
isbn={978-989-758-522-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Control System Design via Constraint Satisfaction using Convolutional Neural Networks and Black Hole Optimization
SN - 978-989-758-522-7
AU - Yaghoobi S.
AU - Fadali M.
PY - 2021
SP - 232
EP - 239
DO - 10.5220/0010618902320239