Abstract
The Cable-Pulley-Driven System (CPDS) is widely used in surgical robots. It is an important foundation for long-distance drive and design of small-sized end-effectors. It is generally used as one of the key components of the minimally invasive surgical machine-driven unit. Compared with the traditional rigid driven system, CPDS is light in weight, compact in structure and flexible in movement. CPDS can complete long-distance and high-load transmission in the narrow and curved space of the human body. However, due to the non-linear characteristics of CPDS, the tension loss of the cable will be caused, and the positioning accuracy and position control performance of the operating device will be significantly affected. This paper proposed a full-closed loop tracking control method for CPDS surgical robotic manipulator with PID position control strategy. This method used an Artificial Neural Network (ANN) for Multi-factor Coupling Compensation (MCC). The feasibility and effectiveness of this method are verified by a series of experimental analyses on a Backdrivable Cable-Driven Series Elastic Actuator (BCDSEA).
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 52175221, U19A20101 and 51805129, in part by the Fundamental Research Funds for the Central Universities under Grant PA2021KCPY0046.
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Yu, X., Liu, G., Wang, Z., Zi, B. (2022). Full-Closed Loop Tracking Control Based on Multi-factor Coupling Compensations Using Artificial Neural Network for a Cable-Pulley-Driven Surgical Robotic Manipulator. In: Larochelle, P., McCarthy, J.M. (eds) Proceedings of the 2022 USCToMM Symposium on Mechanical Systems and Robotics. USCToMM MSR 2022. Mechanisms and Machine Science, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-030-99826-4_5
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