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In recent times, intelligent control systems have attracted significant interest across diverse industries. This study compares Fuzzy Logic Controllers (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers for regulating a DC motor’s speed. FLC uses linguistic variables and fuzzy sets for handling imprecise data, while ANFIS integrates artificial neural networks (ANN) and fuzzy inference systems (FIS) for flexible control. Evaluation metrics include settling time, rise time, overshoot, and steady-state error. Simulations on a DC motor model reveal both controllers’ performance differences. FLC is more straightforward but may struggle with complexity, while ANFIS is adaptable but computationally intensive due to NN training. ANFIS achieves a faster rise time (0.62 s), less overshoot ( \(0.4\%\) ), and zero delays compared to the classical (I-PD) controller (0.73 s, \(0.51\%\) , and one-second delay). However, (I-PD) outperforms settling time (2.3 s versus 3.8 s for ANFIS) with a \(1.0\%\) steady-state error. These results aid in selecting the best control approach.

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Efficiency of FL, ANFIS, and Classical DC Motor Controllers

  • 会议论文

  • 发表日期：

    2025年

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  • Mirna Ali,
  • Nema Salem

摘要

In recent times, intelligent control systems have attracted significant interest across diverse industries. This study compares Fuzzy Logic Controllers (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers for regulating a DC motor’s speed. FLC uses linguistic variables and fuzzy sets for handling imprecise data, while ANFIS integrates artificial neural networks (ANN) and fuzzy inference systems (FIS) for flexible control. Evaluation metrics include settling time, rise time, overshoot, and steady-state error. Simulations on a DC motor model reveal both controllers’ performance differences. FLC is more straightforward but may struggle with complexity, while ANFIS is adaptable but computationally intensive due to NN training. ANFIS achieves a faster rise time (0.62 s), less overshoot ( \(0.4\%\) ), and zero delays compared to the classical (I-PD) controller (0.73 s, \(0.51\%\) , and one-second delay). However, (I-PD) outperforms settling time (2.3 s versus 3.8 s for ANFIS) with a \(1.0\%\) steady-state error. These results aid in selecting the best control approach.