Investigation of tribological interactions influence on dynamics of optimal surgical robot with DC motor and PID controller taking into account inputs from in vitro experiments on cardiovascular tissue

• Autorzy: Ilewicz Grzegorz

Identyfikatory

DOI

10.21008/j.0860-6897.2024.2.01

• Języki publikacji: EN

Abstrakty

EN

Tribological interactions are one of the basic reactions affecting the course of the drive torque in surgical robot joints. It is interesting to test out what is the impact of friction on its dynamics because it gives the possibility to effective control. Inputs from two in vitro experiments on cardiovascular tissue were added to optimization model. For the optimal obtained geometry, a model of dynamics of driving torques was constructed by using the block diagram method, taking into the account the inertia tensors and the locations of masses centers. The electromechanical DC motor model was added to each joint. PID regulator models were also added to them and step response with optimal indicators of the regulation quality was received using gradient descent method. For a specific mechatronic system of the surgical robot, dynamic friction model was formulated based on the Lund-Grenoble model.

Słowa kluczowe

EN

surgical robot; LuGre; NSGA-II; optimization; FEM; PID; in vitro

PL

robot chirurgiczny; LuGre; NSGA-II; optymalizacja; MES; PID; in vitro

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2024
• Tom: Vol. 35, nr 2
• Strony: art. no. 2024201
• Opis fizyczny: Bibliogr. 20 poz., il. kolor., fot., rys., wykr.

Twórcy

autor

Ilewicz Grzegorz

• Institute of Micromechanics and Photonics, Warsaw University of Technology, św. Andrzeja Boboli 8, 02-525 Warsaw, Poland

• https://orcid.org/0000-0003-2561-3893

Bibliografia

• 1. Y. Lingtao, W. Lan, W. Zhengyu, W. Wenjie; Dynamic modeling and analysis for instrument arm based on the physical properties of soft tissue; Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2017, 32(12), 2185-2199
• 2. R. Konietschke, T. Ortmaier, H. Weiss, R. Engelke, G. Hirzinger; Optimal Design of a Medical Robot for Minimally Invasive Surgery; 2003
• 3. W. Wang, W. Wang, W, Dong,H. Yu, Z. Yan, Z. Du; Dimensional optimization of a minimally invasive surgical robot system based on NSGA-II algorithm; Advances in Mechanical Engineering, 2015, 7
• 4. G. Niu, B.Pan, F. Zhang, H.Feng, Y.Fu; Multi-optimization of a spherical mechanism for minimally invasive surgery; Journal of Central South University, 2017, 24, 1406-1417
• 5. I. Buzurovic; Dynamic model of medical robot represented as descriptor system; International Journal of Information and Systems Sciences, 2008, 2(2), 316-333
• 6. V. Hernández-Guzmán, V. Santibáñez, G. Herrera; Control of Rigid Robots Equipped with Brushed DC-Motors as Actuators; International Journal of Control, Automation, and Systems, 2007, 5(6), 718-724
• 7. X. Tu , Y. Zhao P.Zhou; Parameter Identification of Static Friction Based on An Optimal Exciting Trajectory; IOP Conf. Series: Materials Science and Engineering, 2017, 280
• 8. L. Tien, A. Albu-Schafafer, A. De Luca, G. Hirzingerc; Friction Observer and Compensation for Control of Robots with Joint Torque Measurement; 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
• 9. P. Berthet-Rayne, G. Gras, K. Leibrandt, P. Wisanuvej, A. Schmitz, C. Seneci, G. Yang; The i2Snake Robotic Platform for Endoscopic Surgery; Annals of Biomedical Engineering, 2018, 46(10), 1663-1675
• 10. B. Armstrong-Helouvry, P. Dupont, C. Canudas de Wit; A survey of models analysis tools and compensation methods for the control of mechanism with friction; Automatica, 1994, 30, 1083-1138
• 11. C. Canudas de Wit, H. Olsson, K.J .Åström, and etc.; A new model for control of systems with friction; IEEETrans. Autom. Control, 1995, 40(3), 419-425
• 12. K. Johan Åström, C.Canudas de Wit.; Revisiting the LuGre Friction Model. Stick-slip motion and rate dependence; IEEE Control Systems Magazine, 2008, 28(6), 101-114
• 13. O. Muvengei, J. Kihiu, B. Ikua; Computational Implementation of LuGre Friction Law in a Revolute Joint with Clearanc;.Proceedings of the 2012 Mechanical Engineering Conference on Sustainable Research and Innovation, 2012, 4
• 14. G. Ilewicz; Modeling and controlling medical robot for soft tissue surgery and servicing the artificial organs; 17th International Conference on Mechatronics-Mechatronika (ME), 2016, 1-5
• 15. G. Ilewicz, A Harlecki; Multi-objective optimization and linear buckling of serial chain of a medical robot tool for soft tissue surgery; IAES International Journal of Robotics and Automation, 2020, 9(1), 17
• 16. G. Ilewicz; Multibody model of dynamics and optimization of medical robot to soft tissue surgery; Advanced Mechatronics Solutions, 2015, 129-134
• 17. J. Wu, Q. Yan, S.Huang, C. Zou, J. Zhong, W. Wang; Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method; Advances in Civil Engineering, 2018; DOI: 10.1155/2018/8980756
• 18. X. Gao, Z. Qin, Y. Guo, M. Wang, T. Zan; Adaptive Method to Reduce Thermal Deformation of Ball Screws Based on Carbon Fiber Reinforced Plastics; 2019, 12(19), 3113; DOI: 10.1515/ntrev-2022-0029
• 19. X. Song, J. Jung, H. Son, J. Park, K. Lee, J. Park; Metamodel based optimization of a control arm considering strength and durability performance; Computers and Mathematics with Applications, 2012, 60, 976-980
• 20. G. Ilewicz, E. Ładyżyńska-Kozdraś; Specifying Inputs for the Computational Structure of a Surgical System via Optical Method and DLT Algorithm Based on In Vitro Experiments on Cardiovascular Tissue in Minimally Invasive and Robotic Surgery; Sensors, 2022, 22(6), 2335