Scholarlink

The ACO algorithm serves as a tuning mechanism for the PID controller, modifying it to enhance the performance of a DC servo control loopback system. The hybrid controller, formed by integrating the ACO algorithm with the PID controller, aims to optimize the overall control of the system. To achieve a desirable process response characterized by robustness, speed, and precision, the ACO algorithm is utilized as a search technique to determine the optimal gain parameters for the PID controller. This study introduces the design of a DC Servo PID controller design using the ACO algorithm in MATLAB/Simulink. The Ant Colony Optimization (ACO) technique can be employed to optimize PID controller design, leading to improved performance. Enhanced controller design can be attained through the optimization of PID parameters using the Ant Colony Optimization (ACO) algorithm. It analyzed different time parameters of the ACO-tuned PID controller applied to the higher-order transfer function of a DC servo motor system, comparing the results with those obtained from existing algorithms. Simulation outcomes underscore the ACO algorithm’s effectiveness, efficiency, and precision in fine-tuning the PID controller. The ACO algorithm is implemented in Verilog and verified using Modelism.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Designing a PID Controller Using Ant Colony Optimization and Implementing It with FPGA

  • 章节

  • 发表日期：

    2025年

查看全文
  • B. Chandrababu Naik,
  • C. V. Ravi Kumar,
  • Muhsin Jaber Jweeg,
  • Moneer H. Tolephih,
  • M. Hari Prasad,
  • M. N. Mohammed,
  • Oday I. Abdullah

摘要

The ACO algorithm serves as a tuning mechanism for the PID controller, modifying it to enhance the performance of a DC servo control loopback system. The hybrid controller, formed by integrating the ACO algorithm with the PID controller, aims to optimize the overall control of the system. To achieve a desirable process response characterized by robustness, speed, and precision, the ACO algorithm is utilized as a search technique to determine the optimal gain parameters for the PID controller. This study introduces the design of a DC Servo PID controller design using the ACO algorithm in MATLAB/Simulink. The Ant Colony Optimization (ACO) technique can be employed to optimize PID controller design, leading to improved performance. Enhanced controller design can be attained through the optimization of PID parameters using the Ant Colony Optimization (ACO) algorithm. It analyzed different time parameters of the ACO-tuned PID controller applied to the higher-order transfer function of a DC servo motor system, comparing the results with those obtained from existing algorithms. Simulation outcomes underscore the ACO algorithm’s effectiveness, efficiency, and precision in fine-tuning the PID controller. The ACO algorithm is implemented in Verilog and verified using Modelism.