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基于神经网络的学习控制及其在机器人中的应用
引用本文:姚仲舒,吴键荣,杨成梧.基于神经网络的学习控制及其在机器人中的应用[J].电工技术学报,2003,18(3):72-76.
作者姓名:姚仲舒  吴键荣  杨成梧
作者单位:南京理工大学动力学院,210094
摘    要:针对一类非线性系统的跟踪控制问题 ,首先提出了一种遗忘因子迭代学习控制算法 ,给出了算法收敛的充分条件 ,然后 ,利用神经网络原理 ,对要求跟踪的新的期望轨迹 ,在系统的历史控制经验基础上 ,用神经网络估计系统的期望控制输入 ,然后将其作为迭代学习控制器的初始控制输入 ,再由迭代学习律逐步改善控制输入 ,使系统的实际输出只需较少的迭代次数就能达到跟踪的精度要求。机器人系统的仿真结果表明了该算法的有效性。

关 键 词:非线性系统  迭代学习控制  神经网络  轨迹跟踪  机器人
修稿时间:2002年10月14

Neural Network-Based Learning Control and Its Application for Robot
Yao Zhongshu Wu Jianrong Yang Chengwu.Neural Network-Based Learning Control and Its Application for Robot[J].Transactions of China Electrotechnical Society,2003,18(3):72-76.
Authors:Yao Zhongshu Wu Jianrong Yang Chengwu
Affiliation:NanJing University of Science and Technology 210094 China
Abstract:An iterative learning controller based on neural network learning is presented for trajectory-tracked task of a class of nonlinear systems.In the first part of the paper,sufficient condition for learning algorithm with forgetting factor is derived to guaratee convergence of learning system.In the second part of the paper,desired control input of iterative learning controller is estimated by neural networks incorporated experience for a new desired trajectory-tracked task.If the selection of initial control input has considered previous experience of the controller for a new desired trajectory tracked task,then convergence of error can be improved and accuracy of tracking can be full only few iterative number.Simulation examples of robot show their effectiveness.
Keywords:Nonlinear system  iterative learning control  neural networks  trajectory tracking  robot
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