TY - GEN
T1 - Model Predictive Control of a Soft Laparoscope Using Neural Networks
AU - Cespedes, Axel
AU - Terreros, Ricardo
AU - Morales, Sergio
AU - Huamani, Aldair
AU - Fabian, Joao
AU - Canahuire, Ruth
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Soft robots are an emerging alternative to traditional surgical equipment. Specifically, capabilities of these like flexibility and controlled rigidity, are exploited in enclosed environments where requirements can restrict the use of rigid apparatus. In this work, a soft robot intended to perform laparoscopic activities is used. The design of a cascade control system for this soft laparoscope is presented, composed of two control algorithms: one in charge of controlling the position of the robot and the second in charge of controlling the pressure on its inner chambers. The latter is based on a standard ON-OFF controller, and the former is based on the Model Predictive Control (MPC) methodology, where Deep Neural Networks (DNN) are used for modeling the actuator. The results show that our control system allows the soft laparoscope to reach a desired orientation with a maximum error of 0.06 rad.
AB - Soft robots are an emerging alternative to traditional surgical equipment. Specifically, capabilities of these like flexibility and controlled rigidity, are exploited in enclosed environments where requirements can restrict the use of rigid apparatus. In this work, a soft robot intended to perform laparoscopic activities is used. The design of a cascade control system for this soft laparoscope is presented, composed of two control algorithms: one in charge of controlling the position of the robot and the second in charge of controlling the pressure on its inner chambers. The latter is based on a standard ON-OFF controller, and the former is based on the Model Predictive Control (MPC) methodology, where Deep Neural Networks (DNN) are used for modeling the actuator. The results show that our control system allows the soft laparoscope to reach a desired orientation with a maximum error of 0.06 rad.
KW - model predictive control
KW - neural networks
KW - soft laparoscope
KW - soft robots
UR - https://www.scopus.com/pages/publications/85190546815
U2 - 10.1109/ICMCR60777.2024.10482209
DO - 10.1109/ICMCR60777.2024.10482209
M3 - Conference contribution
AN - SCOPUS:85190546815
T3 - 2024 2nd International Conference on Mechatronics, Control and Robotics, ICMCR 2024
SP - 57
EP - 61
BT - 2024 2nd International Conference on Mechatronics, Control and Robotics, ICMCR 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Mechatronics, Control and Robotics, ICMCR 2024
Y2 - 27 February 2024 through 29 February 2024
ER -