A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive surgery

作者：Sang, Hongqiang^*; Yang, Chenghao; Liu, Fen; Yun, Jintian; Jin, Guoguang

来源：International Journal of Medical Robotics and Computer Assisted Surgery, 2016, 12(4): 670-679.

DOI：10.1002/rcs.1784

• friction compensation
• fuzzy neural network sliding mode controller
• minimally invasive surgical robot
• vibration suppression

Background It is very important for robotically assisted minimally invasive surgery to achieve a high-precision and smooth motion control. However, the surgical instrument tip will exhibit vibration caused by nonlinear friction and unmodeled dynamics, especially when the surgical robot system is attempting low-speed, fine motion.
Methods A fuzzy neural network sliding mode controller (FNNSMC) is proposed to suppress vibration of the surgical robotic system. Nonlinear friction and modeling uncertainties are compensated by a Stribeck model, a radial basis RBF) neural network and a fuzzy system, respectively.
Results Simulations and experiments were performed on a 3 degree-of-freedom (DOF) minimally invasive surgical robot. The results demonstrate that the FNNSMC is effective and can suppress vibrations at the surgical instrument tip.
Conclusions The proposed FNNSMC can provide a robust performance and suppress the vibrations at the surgical instrument tip, which can enhance the quality and security of surgical procedures.

• 出版日期2016-12
• 单位天津工业大学