基于RBF神经网络的上肢柔性外骨骼机器人自适应复合控制  

Adaptive Composite Control of an Upper Limb Flexible Exoskeleton Robot Based on RBF Neural Network

作　　者：门曦凯 郭朝 MEN Xikai;GUO Zhao(School of Power and Mechanical Engineering,Wuhan University,Wuhan 430000,China)

机构地区：[1]武汉大学动力与机械学院,湖北武汉430000

出　　处：《控制工程》2025年第4期586-594,共9页Control Engineering of China

基　　金：中国残联残疾人辅助器具专项(2021CDPFAT-27)。

摘　　要：为了提高上肢外骨骼机器人关节的柔性,结合模块化串联弹性驱动器和鲍登线,提出了一种上肢柔性外骨骼机器人。针对鲍登线产生的非线性摩擦、外界未知扰动和模型不确定性,提出了一种基于径向基函数(radial basis function,RBF)神经网络的自适应复合控制器。该控制器采用扰动观测器和RBF神经网络自适应控制器对扰动进行估计和补偿,并通过滑模控制器实现上肢柔性外骨骼机器人的跟踪控制。此外,通过李雅普诺夫理论证明了该控制器的稳定性。仿真结果表明,与传统的比例积分微分(proportional integral differential,PID)控制器和滑模控制器相比,所提控制器具有更好的扰动补偿能力、更高的跟踪控制精度和鲁棒性,实现了对上肢柔性外骨骼机器人的精准跟踪控制。In order to improve the joint flexibility of the upper limb exoskeleton robot,an upper limb flexible exoskeleton robot with modular series elastic actuators and Bowden cable is proposed.To reduce the nonlinear friction,unknown external disturbance and model uncertainty caused by Bowden cable,an adaptive composite controller based on radial basis function(RBF)neural network is proposed.The disturbance observer and RBF neural network adaptive controller are used to estimate and compensate the disturbances,and the sliding mode controller is used to implement the tracking control of the upper limb flexible exoskeleton robot.In addition,the stability of the controller is proved by Lyapunov theory.The simulation results show that the proposed controller has better disturbance compensation capability,higher tracking control accuracy and robustness compared with the conventioal proportional integral differential(PID)controller and sliding mode controller,and can realize the precise tracking control of the upper limb flexible exoskeleton robot.

关 键 词：上肢柔性外骨骼机器人 RBF神经网络 滑模控制 扰动观测器 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]