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Updated: Mar 10, 2026
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Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery
Published on: November 14, 2015
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A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive
Hongqiang Sang1, Chenghao Yang1, Fen Liu1
1School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin, 300387, China.
Summary
A novel fuzzy neural network sliding mode controller (FNNSMC) effectively suppresses surgical robot vibrations. This enhances precision and safety in robotically assisted minimally invasive surgery.
Area of Science:
- Robotics
- Control Systems
- Biomedical Engineering
Background:
- High-precision motion control is crucial for robotically assisted minimally invasive surgery.
- Surgical robot systems experience vibration during low-speed, fine movements due to nonlinear friction and unmodeled dynamics.
Purpose of the Study:
- To propose a controller that suppresses vibration in surgical robotic systems.
- To improve the precision and smoothness of surgical instrument tip motion.
Main Methods:
- A fuzzy neural network sliding mode controller (FNNSMC) was developed.
- Nonlinear friction was compensated using a Stribeck model.
- Modeling uncertainties were addressed by a radial basis function (RBF) neural network and a fuzzy system.
Main Results:
- Simulations and experiments were conducted on a 3-DOF surgical robot.
- The FNNSMC demonstrated effectiveness in suppressing vibrations at the surgical instrument tip.
Conclusions:
- The FNNSMC offers robust performance in vibration suppression for surgical robots.
- This leads to enhanced quality and security in minimally invasive surgical procedures.
Keywords:
friction compensationfuzzy neural network sliding mode controllerminimally invasive surgical robotvibration suppressionMore Related Videos
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