Modelling and control of non-linear tissue compression and heating using an LQG controller for automation in robotic surgery

Abstract

Robot-assisted surgery is being widely used as an effective approach to improve the performance of surgical procedures. Autonomous control of surgical robots is essential for tele-surgery with time delay and increased patient safety. In order to improve safety and reliability of the surgical procedure of tissue compression and heating, a control strategy for simultaneously automating the surgical task is presented in this paper. First, the electrosurgical procedure such as vessel closure that involves tissue compression and heating has been modelled with a multiple-input–multiple-output (MIMO) non-linear system for automation simultaneous. After linearizing the models, the linear-quadratic Gaussian (LQG) is used to control the tissue compression process and tissue heating process, and the particle swarm optimization (PSO) algorithm was used to choose the optimal weighting matrices for the LQG controllers according to the desired controlling accuracy. The LQG controllers with optimal weights were able to track both the tissue compression and temperature references in finite time horizon and with minimal error (tissue compression – the max absolute error was [Formula: see text] m and temperature – the max absolute error is 0.4561°C). Compared with a LQG controller with weighting matrices chosen by trial and error, a PSO-based optimized controller provided the least error and faster convergence. We have developed a control framework for simultaneously automating the surgical tasks of tissue compression and heating in robotic surgery, and modelled the automation of electrosurgical task using LQG controller with optimal weighting matrix obtained using a PSO algorithm.