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<p>The Automatic Voltage Regulator (AVR) has a critical responsibility in sustaining the output voltage of a synchronous generator within desired limits. To enhance its dynamic performance, various control techniques have been integrated into the AVR framework, among which the Sigmoid Proportional-Integral-Derivative (SPID) controller has shown promising results. However, effective tuning of the SPID controller remains a significant challenge due to system nonlinearities and external disturbances, which hinder the achievement of a high dynamic response. To overcome these limitations, this study presents a novel hybrid optimization strategy that combines the Nonlinear Sine Cosine Algorithm (NSCA) with the Safe Experimentation Dynamics (SED) algorithm, termed NSCA-SED, as a data-driven method for tuning the SPID controller in the AVR system. Unlike conventional applications of metaheuristic algorithms focused on machine learning or neural network training, the proposed NSCA-SED algorithm is designed specifically to address measurement and control challenges within AVR systems. The algorithm directly optimizes controller parameters based on measurement feedback, thereby increasing robustness, reducing overshoot, as well as improving settling time and steady-state error. The controller's performance is assessed using time-domain performance indices, including Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), and Integral Time Squared Error (ITSE) as well as parameter uncertainties, Bode plot, and root locus. Simulation results show that the proposed NSCA-SED-SPID controller outperforms other recent metaheuristic-tuned PID controllers across various performance indices as mentioned. These findings demonstrate the method’s relevance and effectiveness in measurement-sensitive control applications particularly in terms of control robustness and transient response.</p>

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Hybrid nonlinear sine cosine and safe experimentation dynamics algorithm for robust sigmoid PID control of automatic voltage regulators

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Mohd Zaidi Mohd Tumari,
  • Mohd Helmi Suid,
  • Mohd Ashraf Ahmad

摘要

The Automatic Voltage Regulator (AVR) has a critical responsibility in sustaining the output voltage of a synchronous generator within desired limits. To enhance its dynamic performance, various control techniques have been integrated into the AVR framework, among which the Sigmoid Proportional-Integral-Derivative (SPID) controller has shown promising results. However, effective tuning of the SPID controller remains a significant challenge due to system nonlinearities and external disturbances, which hinder the achievement of a high dynamic response. To overcome these limitations, this study presents a novel hybrid optimization strategy that combines the Nonlinear Sine Cosine Algorithm (NSCA) with the Safe Experimentation Dynamics (SED) algorithm, termed NSCA-SED, as a data-driven method for tuning the SPID controller in the AVR system. Unlike conventional applications of metaheuristic algorithms focused on machine learning or neural network training, the proposed NSCA-SED algorithm is designed specifically to address measurement and control challenges within AVR systems. The algorithm directly optimizes controller parameters based on measurement feedback, thereby increasing robustness, reducing overshoot, as well as improving settling time and steady-state error. The controller's performance is assessed using time-domain performance indices, including Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), and Integral Time Squared Error (ITSE) as well as parameter uncertainties, Bode plot, and root locus. Simulation results show that the proposed NSCA-SED-SPID controller outperforms other recent metaheuristic-tuned PID controllers across various performance indices as mentioned. These findings demonstrate the method’s relevance and effectiveness in measurement-sensitive control applications particularly in terms of control robustness and transient response.