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<p>One method for a surgeon to control a camera-holding robot during laparoscopic surgery without removing their hands from the surgical tools involves recognizing and tracking the surgical instruments. Although image processing is crucial, a control algorithm that smoothly operates based on the pixel coordinates obtained from the image processing is equally important. This paper proposes a control algorithm combining a Hammerstein model and model predictive control (HMPC) to maintain consistent control performance despite the zoom in/out of the endoscopic camera and the changes in the distance between the camera and the instruments, significantly affecting the control performance. Through experiments, a Hammerstein model was derived by extracting a nonlinear static model and a linear dynamic model. The performance of the HMPC in tracking surgical instruments was verified under static operations such as step inputs and dynamic operations following a trajectory. The experimental results indicated that the proposed algorithm enhanced the adaptability and consistency in stabilization time under various conditions, ensuring robust and stable tracking performance while effectively mitigating the impact of distance changes.</p>

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Model Predictive Control for Consistent Tool-tracking of a Surgical Assistant Robot Under Laparoscopic Zoom Changes

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Youqiang Zhang,
  • Minhyo Kim,
  • Jun Seok Park,
  • Sangrok Jin

摘要

One method for a surgeon to control a camera-holding robot during laparoscopic surgery without removing their hands from the surgical tools involves recognizing and tracking the surgical instruments. Although image processing is crucial, a control algorithm that smoothly operates based on the pixel coordinates obtained from the image processing is equally important. This paper proposes a control algorithm combining a Hammerstein model and model predictive control (HMPC) to maintain consistent control performance despite the zoom in/out of the endoscopic camera and the changes in the distance between the camera and the instruments, significantly affecting the control performance. Through experiments, a Hammerstein model was derived by extracting a nonlinear static model and a linear dynamic model. The performance of the HMPC in tracking surgical instruments was verified under static operations such as step inputs and dynamic operations following a trajectory. The experimental results indicated that the proposed algorithm enhanced the adaptability and consistency in stabilization time under various conditions, ensuring robust and stable tracking performance while effectively mitigating the impact of distance changes.