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<p>Micro-robotics employed for targeted drug delivery requires precise and robust control in a very perturbed biological environment. This study proposes the optimization of control laws for micro-robotic systems based on the Greylag Goose Optimization Algorithm (GGOA), applied to a Proportional-Integral-Derivative (PID) controller and a sliding mode control (SMC). The parameters are automatically tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE), and the performances of the two methods are compared. The simulation results demonstrate that the GGOA-PID approach reduces the overshoot by up to 99.99% and the settling time by approximately 78.7% compared to the GGOA-SMC, confirming the pertinence of the optimized PID for the control of micro-robotic systems to be used in biomedical applications.</p>

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Intelligent control strategies for microrobot positioning

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Mohamed Kmich,
  • Hicham Karmouni,
  • Mhamed Sayyouri

摘要

Micro-robotics employed for targeted drug delivery requires precise and robust control in a very perturbed biological environment. This study proposes the optimization of control laws for micro-robotic systems based on the Greylag Goose Optimization Algorithm (GGOA), applied to a Proportional-Integral-Derivative (PID) controller and a sliding mode control (SMC). The parameters are automatically tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE), and the performances of the two methods are compared. The simulation results demonstrate that the GGOA-PID approach reduces the overshoot by up to 99.99% and the settling time by approximately 78.7% compared to the GGOA-SMC, confirming the pertinence of the optimized PID for the control of micro-robotic systems to be used in biomedical applications.