基于RBF神经网络的双臂手术机器人自适应导纳控制

Adaptive admittance control for dual-arm surgical robot using radial basis function neural network

针对双臂机器人在辅助头颈部手术拉开软组织过程中环境刚度变化而导致的力跟踪误差较大问题,提出一种基于径向基函数(RBF)神经网络的自适应导纳控制策略,减小力跟踪误差,提升系统的响应速度。通过在手术过程中利用RBF神经网络在线调整导纳参数,提高机械臂对不同接触条件和操作要求的适应性,实现快速精确的力跟踪。仿真实验将基于RBF神经网络的自适应导纳控制策略引入双臂力同步导纳控制系统并与传统定参数导纳控制对比,证明其在接触环境参数变化情况下的接触力控制效果。结果表明,基于RBF神经网络的自适应导纳控制策略可以有效提升双臂手术机器人力跟踪精度、响应速度以及抗干扰能力。

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.