基于迭代学习控制算法的下肢外骨骼机器人跟随特性研究

目前下肢外骨骼机器人存在的运动控制算法追踪人体髋关节和膝关节期望轨迹时存在误差，从而导致人机系统随动性能差。因此，本文提出迭代学习控制算法追踪人体髋关节和膝关节期望轨迹。首先，结合人体下肢结构分析，建立下肢外骨骼机器人动力学模型；其次，基于迭代学习控制算法建立下肢外骨骼机器人随动控制模型；最后，利用Matlab软件设计指数变增益闭环D型运动控制系统，分析收敛速度与谱半径的关系，追踪得到人体下肢髋关节和膝关节期望轨迹。仿真结果表明该算法能够有效提高下肢外骨骼机器人步态轨迹跟踪精度，提升人机系统随动性能。

Follow-up Characteristics of Lower Limb Exoskeleton Robot Based on Iterative Learning Control Algorithm

Currently, motion control algorithms of lower limb exoskeleton robot had errors in tracking desired trajectories of human hip and knee joints, resulting in poor follow-up performance of the human-robot system. Therefore, this paper proposed Iterative Learning Control Algorithm to track the desired trajectory of human hip and knee joints. Firstly, considering the analysis of lower limb human structure, the dynamic model of lower limb exoskeleton robot was established. Secondly, the follow-up control model of lower limb exoskeleton robot was established, which based on Iterative Learning Control Algorithm. Finally, the exponential gain close-loop D type motion control system was designed, and the relationship between spectral radius and the convergence speed were analyzed. The hip and knee joints expected trajectory was tracked based on Matlab software. The simulation results show that this algorithm can effectively improve the accuracy of gait trajectory tracking, and the follow-up of human-robot system is promoted.