动态约束下可重构模块机器人自适应滑模控制方法  

Adaptive sliding mode control method for reconfigurable module robot under dynamic constraints

作　　者：刘先学 李婕 Liu Xianxue;Li Jie(Zhucheng Office Affairs Service Center of Shandong Province,Shandong Zhucheng,262200,China)

机构地区：[1]山东省诸城市机关事务服务中心,山东诸城262200

出　　处：《机械设计与制造工程》2022年第8期39-44,共6页Machine Design and Manufacturing Engineering

基　　金：2021年山东省高新技术企业研发项目(ZGPM2021-07)。

摘　　要：针对目前机器人滑模控制方法自适应性能较差、末端约束力较弱、轨迹跟踪效果不佳的问题,提出基于动态约束的可重构模块机器人自适应滑模控制方法。首先根据运动学原理分析机器人各模块间的运动学关系,以牛顿-欧拉迭代方程为基础建立动力学模型;然后采用等效控制RBF神经网络的方法,调整机器人神经网络权值;最后根据RBF神经网络调整机器人在滑模控制方面的增益,完成对机器人的自适应滑模控制。实验结果说明,该方法具备较好的抑制抖动效果,轨迹跟踪性能高且自适应性能较强,能在动态约束条件下实现机器人的有效控制。Aiming at the problems of poor adaptive performance,weak end restraint force and poor trajectory tracking effect of current robot sliding mode control methods,an adaptive sliding mode control method for reconfigurable modular robots based on dynamic constraints is proposed.Firstly,the kinematic relationship between the modules of the robot is analyzed according to the principle of kinematics,and the dynamic model is established based on the Newton-Euler iterative equation.Then the equivalent control RBF neural network method is used to adjust the weights of the robot neural network.Finally,according to the RBF the neural network adjusts the gain of the robot in sliding mode control,and completes the adaptive sliding mode control of the robot.The experimental results show that this method has good effects of suppressing jitter,high track tracking performance and strong adaptive performance,and can realize effective control of the robot under dynamic constraints.

关 键 词：动态约束 自适应 滑模控制 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]