Model predictive control for compensation of cyclic organ motions in teleoperated laparoscopic surgery

During teleoperated laparoscopic surgery with robots, periodic deformations of organs due to respiratory movements may be critical disturbances for surgeons manipulating the robot. Indeed, the surgeon has to manually compensate for these motions if accurate gestures are needed, like, e.g., during suturing. This paper proposes a repetitive model predictive control scheme for driving a surgical robot toward the reference trajectory defined by the surgeon while rejecting cyclic disturbances due to the respiratory motion. A new cost function is proposed for an unconstrained generalized predictive control scheme based on a multiple input-output model of the surgical robot. Contributions of the controller output to the reference tracking task and to the disturbance rejection task are split and computed separately, so that their respective efficiency can be independently weighted. The proposed control scheme is validated through simulations and experimental in vivo results using a surgical robot.