控制理论(社会学)
计算机科学
控制器(灌溉)
李雅普诺夫函数
弹道
运动学
指数稳定性
Lyapunov稳定性
人工神经网络
理论(学习稳定性)
控制工程
控制(管理)
人工智能
工程类
物理
经典力学
非线性系统
量子力学
天文
机器学习
农学
生物
作者
Yuanchun Li,Ruikang Fan,Tianjiao An,Bo Dong,Bing Ma
标识
DOI:10.1109/ccdc58219.2023.10327617
摘要
In this paper, a distributed coordination control of dual-arm reconfigurable manipulators based on neural networks learning is presented for handling tasks. Based on Newton-Euler algorithm, the dynamic models of the manipulators and grasped object are established through kinematic analyze, respectively. According to the load distribution method, the motion-induced force is effectively distributed to each arm of the manipulator, and then the dynamics of the single reconfigurable manipulator is obtained. An improved sliding mode function is designed to reflect the error of the joint trajectory and internal force. The radial basis function neural network (RBFNN) is utilized to learn the uncertain dynamics of reconfigurable manipulator. Then the model-free distributed coordination controller is obtained. The asymptotic stability of dual-arm reconfigurable manipulators is proved through Lyapunov stability theory. Finally, the validity of the model-free distributed coordination controller is verified by simulations.
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