TY - JOUR
T1 - Modelling and control of non-linear tissue compression and heating using an LQG controller for automation in robotic surgery
AU - Li, Baichun
AU - Sinha, Utkarsh
AU - Sankaranarayanan, Ganesh
N1 - Publisher Copyright:
© The Author(s) 2016.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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 9. 6057 × 10 -5 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.
AB - 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 9. 6057 × 10 -5 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.
KW - LQG
KW - MIMO
KW - PSO
KW - robot-assisted surgery
KW - tissue compression and heating
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U2 - 10.1177/0142331215596062
DO - 10.1177/0142331215596062
M3 - Article
AN - SCOPUS:84995755307
SN - 0142-3312
VL - 38
SP - 1491
EP - 1499
JO - Transactions of the Institute of Measurement and Control
JF - Transactions of the Institute of Measurement and Control
IS - 12
ER -