Adaptive admittance control for dual-arm surgical robot using radial basis function neural network
10.3969/j.issn.1005-202X.2024.02.012
- VernacularTitle:基于RBF神经网络的双臂手术机器人自适应导纳控制
- Author:
Yan ZHANG
1
;
Zhi HU
Author Information
1. 上海工程技术大学电子电气工程学院,上海 201620
- Keywords:
adaptive admittance control;
radial basis function;
neural network;
dual-arm surgical robot;
force tracking
- From:
Chinese Journal of Medical Physics
2024;41(2):198-204
- CountryChina
- Language:Chinese
-
Abstract:
Aiming at the problem of large force tracking errors caused by environmental stiffness changes when dual-arm robot is assisting in opening soft tissues in head and neck surgery,an adaptive admittance control strategy based on radial basis function(RBF)neural network is proposed for reducing force tracking error and improving system response speed.By using RBF neural network to adjust admittance parameters online during surgery,the adaptability of the robotic arm to different contact conditions and operation requirements is improved,thereby realizing fast and accurate force tracking.The simulation experiment introduces the adaptive admittance control strategy based on RBF neural network into the dual-arm force synchronous admittance control system and compares it with the traditional fixed-parameter admittance control to prove its contact force control effect under the condition of variable contact environment parameters.The results demonstrate that the adaptive admittance control strategy based on RBF neural network can effectively improve the force tracking accuracy,response speed and anti-interference capability of dual-arm surgical robot.
