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Showing 2 results for Genetic Algorithm (ga)

A. A. Khodadoost Arani, J. S. Moghani, A. Khoshsaadat, G. B. Gharehpetian,
Volume 12, Issue 2 (6-2016)
Abstract

Multilevel voltage source inverters have several advantages compare to traditional voltage source inverter. These inverters reduce cost, get better voltage waveform and decrease Total Harmonic Distortion (THD) by increasing the levels of output voltage. In this paper Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are used to find the switching angles for achieving to the minimum THD for output voltage waveform of the Cascaded H-bridge Multi-Level Inverters (MLI). These methods are used for a 27-level inverter for different modulation indices. Result of two methods is identical and in comparison to other methods have the smallest THD. To verify results of two mentioned methods, a simulation using MATLAB/Simulink software is presented.


Suhail Mahmoud Abdullah, Thamir Hassan Atyia,
Volume 21, Issue 4 (11-2025)
Abstract

Optimal control of DC motors remains a critical research area in modern control systems, given their wide industrial applications and the need for accurate performance under variable conditions. This paper explores the application of genetic algorithms (GAs) to optimize the control parameters of DC motors, particularly PID controllers, with the goal of improving the dynamic response and robustness of DC motor systems. Compared to traditional constraint-based tuning methods, GAs, inspired by natural selection and evolution, offer comprehensive search capabilities that significantly improve parameter optimization, providing better speed regulation, reduced overshoot, and minimal steady-state error. This review highlights the key challenges faced when using GAs. Comparative results from various studies demonstrate that GA-based controllers consistently outperform traditional tuning methods in terms of stability, efficiency, and adaptability. Key findings related to energy consumption and stability are highlighted. It is essential to analyze the system performance in terms of rise time (tr), settling time (ts), overshoot ratio (Mp%), and steady-state error (Ess). A proportional-integral-differential (PID) controller provides a stable response by tuning its parameters according to a specific methodology using a genetic algorithm. This paper concludes by emphasizing the potential of genetic generators as a powerful and flexible optimization tool for intelligent control of DC motors.

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