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针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案.同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法.仿真结果表明,该方法是有效的,其控制性能优于PID迭代学习控制系统. 相似文献
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针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案。同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法。仿真结果表明,该方法是有效的,其控制性能优于PID送代学习控制系统。 相似文献
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对近年来注塑机注射速度控制方法进行研究,典型的控制方法可以分为传统控制方法、自适应控制方法、智能控制方法。阐述各种方法的研究现状、应用成果及其优缺点。注射速度是整个注射过程中非常关键的控制变量。最后对注射速度控制的发展做展望。 相似文献
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鳗鱼机器人的动力学模型非线性强、高度欠驱动,导致多关节鳗鱼机器人的切向速度跟踪控制极具挑战.本文采用P型迭代学习控制与步态生成器相结合的方法对多关节鳗鱼机器人的切向速度进行跟踪控制.首先,采用解析牛顿-欧拉法建立非惯性系下的鳗鱼机器人动力学模型,直接获得切向速度子动力学模型;然后,利用带饱和函数的P型迭代学习控制器控制步态参数,并且利用复合能量函数和切向速度子动力学模型分析该控制器的收敛性,得到切向速度跟踪误差的收敛条件;最后,提出鳗鱼机器人的运动控制框架,并对多模块的鳗鱼机器人进行仿真和实验.实验结果表明,实际的切向速度随着迭代次数的增加而逐渐跟踪上了期望的切向速度,故而验证了鳗鱼机器人切向速度跟踪控制器的有效性. 相似文献
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为了提高康复步行训练机器人的跟踪精度及安全性,提出了一种带有运动速度约束和部分记忆信息的自适应迭代学习控制方法,目的是抑制人机不确定性及速度突变对系统跟踪性能的影响.在考虑人机不确定性的基础上,建立了康复步行训练机器人的动力学模型.提出了基于模型预测的速度约束方法,通过限制每个轮子的运动速度,约束了机器人的实际运动速度.进一步,利用受约束的运动速度建立了动力学跟踪误差系统,提出了具有部分记忆信息的自适应迭代学习控制器设计方法,并验证了跟踪误差系统的稳定性.仿真对比分析和实验研究结果表明,文中提出的控制方法能抑制人机不确定性并使康复者在安全速度下完成步行训练. 相似文献
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Chi Zhu Yasumichi Aiyama Tamio Arai Atsuo Kawamura 《Journal of Intelligent and Robotic Systems》2006,46(4):383-404
In order to improve the positioning precision of the stop posture (position and orientation) of an object and decrease the trial numbers in our proposed releasing manipulation, two iterative learning control (ILC) schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in experimental-oriented way. These two methods are all based on a linearized system model. The experimental results show that these methods are effective. Having discussed the characteristics of these control methods, we conclude that in the case there is no enough system knowledge, LCBCC is the only choice to be used to learn the system knowledge; after the enough experience has been acquired, LCBOP is better than LCBCC, in the view of both of the convergent rate and the precision. 相似文献
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《Knowledge and Data Engineering, IEEE Transactions on》2006,18(10):1435-1440
Recent research in machine learning, data mining, and related areas has produced a wide variety of algorithms for cost-sensitive (CS) classification, where instead of maximizing the classification accuracy, minimizing the misclassification cost becomes the objective. These methods often assume that their input is quality data without conflict or erroneous values, or the noise impact is trivial, which is seldom the case in real-world environments. In this paper, we propose a Cost-guided Iterative Classification Filter (CICF) to identify noise for effective CS learning. Instead of putting equal weights on handling noise in all classes in existing efforts, CICF puts more emphasis on expensive classes, which makes it attractive in dealing with data sets with a large cost-ratio. Experimental results and comparative studies indicate that the existence of noise may seriously corrupt the performance of the underlying CS learners and by adopting the proposed CICF algorithm, we can significantly reduce the misclassification cost of a CS classifier in noisy environments. 相似文献
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在同一迭代学习控制(Iterative learning control, ILC)系统中, 选取一个合适的初次迭代控制信号相对于从零开始学习达到目标跟踪精度的迭代次数更少.本文针对线性系统研究从历次轨迹跟踪控制信息中通过期望轨迹匹配提取初次迭代控制信号的方法.首先提出了一种轨迹基元优化匹配算法, 在满足一定相似度的情况下, 通过轨迹分割、平移与旋转变换, 在轨迹基元库中寻找与当前期望轨迹叠合的轨迹基元组合轨迹; 进而, 依据线性叠加原理和轨迹叠合的平移矢量与旋转变换矩阵, 获取与期望轨迹叠合的轨迹基元控制信号; 在此基础上, 通过轨迹基元控制信号串联组合和时间尺度变换, 提取出当前期望轨迹的初次迭代控制信号.对于初次迭代控制信号在拼接处由边界条件差异引起的干扰, 给出了一种${H_\infty }$反馈辅助ILC方法.最后, 在$XYZ$三轴运动平台实现所提算法, 实验结果表明本文所提方法的有效性. 相似文献
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Recent Advances in Iterative Learning Control 总被引:6,自引:0,他引:6
Jian-Xin XU 《自动化学报》2005,31(1):132-142
In this paper we review the recent advances in three sub-areas of iterative learning control (ILC): 1) linear ILC for linear processes, 2) linear ILC for nonlinear processes which are global Lipschitz continuous (GLC), and 3) nonlinear ILC for general nonlinear processes. For linear processes, we focus on several basic configurations of linear ILC. For nonlinear processes with linearILC, we concentrate on the design and transient analysis which were overlooked and missing for a long period. For general classes of nonlinear processes, we demonstrate nonlinear ILC methods based on Lyapunov theory, which is evolving into a new control paradigm. 相似文献
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International Journal of Control, Automation and Systems - The iterative learning control (ILC) is attractive for its simple structure, easy implementation. So the ILC is applied to various fields.... 相似文献