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Parameter Tuning of PID Controller Based on Arithmetic Optimization Algorithm in IOT Systems

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Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1038))

Abstract

Nowadays, the Internet of Things (IOT) is a modernistic growth in industrial applications. Proportional–Integral–Derivative (PID) controller is an essential device in most engineering applications. The vital abuse of PID is choosing the best parameters' values (Kp, Ki, and Kd) with the traditional methods which cannot achieve the optimum response. In this work, Arithmetic Optimization Algorithm (AOA) which is a meta-heuristic algorithm is used to select the optimum values of the parameters. AOA is chosen since it has efficient exploration and exploitation capabilities in the search space. The AOA algorithm is tested for choosing the optimum values of PID's parameters for controlling two engineering applications are three cascaded liquid level tanks systems and DC motor regulation. AOA has superiority over the comparative algorithms which is used in the test.

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Author information

Authors and Affiliations

  1. Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt

    Mohamed Issa

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  1. Mohamed Issa
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Correspondence to Mohamed Issa .

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Editors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein

  2. Faculty of Computer Science & Engineering, Galala University, Suze, Egypt

    Mohamed Abd Elaziz

  3. Department of Computer Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  4. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

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Issa, M. (2022). Parameter Tuning of PID Controller Based on Arithmetic Optimization Algorithm in IOT Systems. In: Houssein, E.H., Abd Elaziz, M., Oliva, D., Abualigah, L. (eds) Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems. Studies in Computational Intelligence, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-99079-4_15

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