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<p>In comparison to a classical PID controller, the optimization of a fractional-order PID controller poses a significant challenge on account of increased complexity due to the presence of five tuning parameters. This study proposes the optimization of fractional-order PID controller optimization by employing a cultural algorithm, which is an evolutionary optimization technique. Unlike traditional methods like particle swarm optimization (PSO) and bacteria foraging optimization (BFO), cultural algorithms (CAs) feature a dual inheritance system and structured belief space. This study uniquely examines the impact of different objective functions on optimization outcomes. Additionally, a comprehensive comparative analysis is conducted to analyze the control performance of conventionally tuned PID controller and CA-optimized PID controller with CA-optimized FOPID controller. The effectiveness of the proposed approach is evaluated on an integer-order system model of brushless direct current (BLDC) motor and a fractional-order model of thermal system. The results show that CAs effectively minimize the objective functions in order to determine controller parameters. The CA-optimized FOPID controllers outperform conventional tuned PID controller as well as CA-optimized PID controller.</p>

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Leveraging cultural algorithms for FOPID controllers with objective function analysis

  • 期刊论文

  • 发表日期：

    2025年1月17日

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  • Tigmanshu Patel,
  • Upesh Patel,
  • Trushit Upadhyaya,
  • Vishal Shah

摘要

In comparison to a classical PID controller, the optimization of a fractional-order PID controller poses a significant challenge on account of increased complexity due to the presence of five tuning parameters. This study proposes the optimization of fractional-order PID controller optimization by employing a cultural algorithm, which is an evolutionary optimization technique. Unlike traditional methods like particle swarm optimization (PSO) and bacteria foraging optimization (BFO), cultural algorithms (CAs) feature a dual inheritance system and structured belief space. This study uniquely examines the impact of different objective functions on optimization outcomes. Additionally, a comprehensive comparative analysis is conducted to analyze the control performance of conventionally tuned PID controller and CA-optimized PID controller with CA-optimized FOPID controller. The effectiveness of the proposed approach is evaluated on an integer-order system model of brushless direct current (BLDC) motor and a fractional-order model of thermal system. The results show that CAs effectively minimize the objective functions in order to determine controller parameters. The CA-optimized FOPID controllers outperform conventional tuned PID controller as well as CA-optimized PID controller.