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A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive surgery
Author(s) -
Sang Hongqiang,
Yang Chenghao,
Liu Fen,
Yun Jintian,
Jin Guoguang
Publication year - 2016
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1784
Subject(s) - computer science , artificial neural network , controller (irrigation) , vibration , mode (computer interface) , invasive surgery , control theory (sociology) , medicine , surgery , artificial intelligence , acoustics , physics , biology , control (management) , operating system , agronomy
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 function (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.