基于优化迭代学习算法的机器人运行轨迹跟踪控制  

Trajectory Tracking Control of Robot Based on Optimized Iterative Learning Algorithm

作　　者：毛羽[1] MAO Yu(Yibin Vocational&Technical College,Yibin Sichuan 644003,China)

机构地区：[1]宜宾职业技术学院,四川宜宾644003

出　　处：《机床与液压》2021年第11期45-50,56,共7页Machine Tool & Hydraulics

基　　金：院级科研项目(ybzysc 20-74)。

摘　　要：为控制机器人跟踪期望轨迹或使其达到指定的目标位姿,设计基于优化迭代学习算法的机器人运行轨迹跟踪控制模型。联合四驱WMR机械构架、主控传感器等交互型应用控制模块,在处理机器人运行轨迹信号的同时,遵照迭代学习算法的极值收敛性优化原理,建立关键动力学模型,实现对机器人运行轨迹的实时跟踪与控制。设计仿真对比实验,证实优化迭代学习算法对机器人运行轨迹的控制有效性。In order to control robot to track the desired trajectory or make it reach the specified target position and pose,a trajectory tracking control model based on the optimized iterative learning algorithm was designed.The interactive application control modules,such as the four-wheel drive WMR mechanical framework and the master control sensor,were combined to process the trajectory signal of the robot and a key dynamic model was established in accordance with the optimization principle of extreme convergence of iterative learning algorithm,so as to realize real-time tracking and control of the trajectory of the robot.Simulation experiments were designed to verify the effectiveness of the optimal iterative learning algorithm for the control of the robot’s trajectory.

关 键 词：优化迭代学习算法 轨迹跟踪 动力学模型 极值收敛性 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]