A Novel Modified Tornado optimizer with Coriolis force Based On Levy Flight to Optimize Proportional Integral Derivative Parameters of DC Motor

One kind of electric motor that runs on direct current (DC) is called a DC (Direct Current) motor. This motor uses the interaction of electric current and magnetic fields to transform electrical energy into mechanical energy, or motion. Applications requiring exact speed and torque control frequently use DC motors. By minimizing errors (differences between setpoints and actual values), proportional-integral-derivative (PID) control is a control technique used to govern dynamic systems to reach desired conditions (setpoints). PID creates an ideal control signal by combining three elements. The Modified Tornado optimizer-based Coriolis force (TOC) method for DC motor control is presented in this article. The paradigm for the TOC approach is the Tornado Optimizer-Based Coriolis Force Algorithm, a metaheuristic that leverages tornado dynamics and the effect of the Coriolis force to address difficult optimization problems. According to this study, the TOC method can be improved by implementing the Levy Flight methodology. According to the results of tests employing optimal functions, the LTOC technique may broaden exploration and exploitation. Meanwhile, when the LTOC technique is applied as a DC motor controller, the optimal overshoot response value is achieved. The LTOC approach outperforms the TOC method by 0.014% and 0.037%, respectively, in terms of ITSE and ITAE values.

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Publication Details

Journal
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Published
2025-08-20
DOI
https://doi.org/10.26740/vubeta.v2i3.39269
Primary Topic
Computational Physics and Python Applications
Type
article
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article

A Novel Modified Tornado optimizer with Coriolis force Based On Levy Flight to Optimize Proportional Integral Derivative Parameters of DC Motor

Farhad Soleimanian Gharehchopogh, Asmunin Asmunin, Widi Aribowo, Vugar Hacimahmud Abdullayev et al.
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Computational Physics and Python Applications
article

A Novel Modified Tornado optimizer with Coriolis force Based On Levy Flight to Optimize Proportional Integral Derivative Parameters of DC Motor

Farhad Soleimanian Gharehchopogh, Asmunin Asmunin, Widi Aribowo, Vugar Hacimahmud Abdullayev, Andi Iwan Nurhidayat
article en

Abstract

One kind of electric motor that runs on direct current (DC) is called a DC (Direct Current) motor. This motor uses the interaction of electric current and magnetic fields to transform electrical energy into mechanical energy, or motion. Applications requiring exact speed and torque control frequently use DC motors. By minimizing errors (differences between setpoints and actual values), proportional-integral-derivative (PID) control is a control technique used to govern dynamic systems to reach desired conditions (setpoints). PID creates an ideal control signal by combining three elements. The Modified Tornado optimizer-based Coriolis force (TOC) method for DC motor control is presented in this article. The paradigm for the TOC approach is the Tornado Optimizer-Based Coriolis Force Algorithm, a metaheuristic that leverages tornado dynamics and the effect of the Coriolis force to address difficult optimization problems. According to this study, the TOC method can be improved by implementing the Levy Flight methodology. According to the results of tests employing optimal functions, the LTOC technique may broaden exploration and exploitation. Meanwhile, when the LTOC technique is applied as a DC motor controller, the optimal overshoot response value is achieved. The LTOC approach outperforms the TOC method by 0.014% and 0.037%, respectively, in terms of ITSE and ITAE values.

Vokasi UNESA Bulletin of Engineering, Technology and Applied ScienceVol. 2(3)
Azerbaijan State Oil and Industry University (AZ), University of Surabaya (ID), Universidad de Guadalajara (MX), Universitas Negeri Surabaya (ID), Islamic Azad University of Urmia (IR)
Openalex Percentile: Top 86%
Computational Physics and Python Applications
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