Predictive Model Reference Adaptive Controller to Compensate Heart Motion in Minimally Invasive CABG Surgery.

Abstract

Heart beating is a major challenge in minimally invasive coronary artery surgery. A promising solution is to develop a motion compensation robotic system that gives the surgeon an impression of operating on motionless tissue by synchronizing the surgical tool automatically with the heart tissue motion. To achieve higher control accuracy, an intelligent controller called Predictive Model Reference Adaptive Controller is presented herein, which is adapted not only by observed reference signals but, also by unknown reference signals that are not observed by a camera but could be predicted by a state space estimator. To develop such a system, first the heart surface motion is tracked by the Lucas-Kanade tracking algorithm and validated by human observation. The results of implementing this control algorithm on a real human heart data set show capability of achieving a motion compensation system with high control accuracy.