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可重构机械臂分散自适应迭代学习控制 被引量：2

Decentralized adaptive iterative learning control for reconfigurable manipulators
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摘要 基于Lyapunov稳定性理论和Backstepping技术,提出了一种可重构机械臂分散自适应迭代学习控制方法。将可重构机械臂动力学系统描述为一个交联子系统的集合,基于Backstepping技术,给出自适应迭代学习控制方法。为了补偿系统模型不确定项和子系统之间交联项,采用自适应神经网络进行逼近。最后,通过数值仿真验证了所提方法的有效性。 Based on I.yapunov stability theory and Backstepping technology, a decentralized adaptive iterative learning control algorithm for reconfigurable manipulators was proposed. The dynamics of the reconfigurable manipulators was represented as a set of interconnected subsystems. An adaptive iterative learning control method based on backstepping technology was proposed. Adaptive neural network was introduced to compensate the unknown term and the interconnection term of each subsystem. Simulation examples were presented to demonstrate the effectiveness of the proposed decentralized controller.
作者 李元春 朱路 董博 刘克平
机构地区 长春工业大学控制工程系 吉林大学汽车仿真与控制国家重点实验室
出处 《吉林大学学报（工学版）》 EI CAS CSCD 北大核心 2012年第2期469-475,共7页 Journal of Jilin University:Engineering and Technology Edition
基金 国家自然科学基金项目(60974010 60674091) 吉林省科技发展计划项目(20110705)
关键词 自动控制技术 可重构机械臂 神经网络 迭代学习控制 Backstepping技术 automatic control technology reconfigurable manipulator neural network iterative learningcontrol Backstepping design
分类号 TP273 [自动化与计算机技术—检测技术与自动化装置]
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参考文献15

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二级参考文献18

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共引文献10

同被引文献14

引证文献2

二级引证文献6

吉林大学学报（工学版）
2012年 第2期

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