共查询到19条相似文献,搜索用时 98 毫秒
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间歇过程最优迭代学习控制的发展:从基于模型到数据驱动 总被引:1,自引:0,他引:1
本文综述了间歇过程的基于模型的和数据驱动的最优迭代学习控制方法.基于模型的最优迭代学习控制方法需要已知被控对象精确的线性模型,其研究较为成熟和完善,有着系统的设计方法和分析工具.数据驱动的最优迭代学习控制系统设计和分析的关键是非线性重复系统的迭代动态线性化.本文简要综述了基于模型的最优迭代学习控制的研究进展,详细回顾了数据驱动的迭代动态线性化方法,包括其详细的推导过程和突出的特点.回顾和讨论了广义的数据驱动最优迭代学习控制方法,包括完整轨迹跟踪的数据驱动最优迭代学习控制方法,提出和讨论了多中间点跟踪的数据驱动最优点到点迭代学习控制方法,和终端输出跟踪的数据驱动最优终端迭代学习控制方法.进一步,迭代学习控制研究中的关键问题,如随机迭代变化初始条件、迭代变化参考轨迹、输入输出约束、高阶学习控制律、计算复杂性等.本文突出强调了基于模型的和数据驱动的最优迭代学习控制方法各自的特点与区别联系,以方便读者理解.最后,本文提出数据驱动的迭代学习控制方法已成为越来越复杂间歇过程控制发展的未来方向,一些开放的具有挑战性的问题还有待于进一步研究. 相似文献
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对于非线性迭代学习控制问题,提出基于延拓法和修正Newton法的具有全局收敛性的迭代学习控制新方法.由于一般的Newton型迭代学习控制律都是局部收敛的,在实际应用中有很大局限性.为拓宽收敛范围,该方法将延拓法引入迭代学习控制问题,提出基于同伦延拓的新的Newton型迭代学习控制律,使得初始控制可以较为任意的选择.新的迭代学习控制算法将求解过程分成N个子问题,每个子问题由换列修正Newton法利用简单的递推公式解出.本文给出算法收敛的充分条件,证明了算法的全局收敛性.该算法对于非线性系统迭代学习控制具有全局收敛和计算简单的优点. 相似文献
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高速列车车内压力波动过大会对乘客舒适性造成影响,而气压模拟系统是一套通过对车内模拟气压跟踪控制,实现对乘客舒适性进行研究的装置.为解决系统历史运行数据利用率低以及存在迭代初始误差导致系统收敛速度慢的问题,采用k最近邻(kNN)算法,建立一种基于历史控制信息的最优初次控制信号提取方法,并根据迭代学习控制的基本原理,将最优控制初值输入到带遗忘因子的迭代学习控制器中,通过不断迭代来实现车内期望气压轨迹的跟踪控制,并和基于大数据的迭代学习控制以及传统PID迭代学习控制进行对比分析.仿真结果表明:基于多步kNN的遗忘迭代学习控制收敛速度更快、系统抖动程度更小、控制精度更高以及算法鲁棒性更好. 相似文献
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基于迭代学习的农业车辆路径跟踪控制 总被引:4,自引:0,他引:4
由于农作物的播种、收获、除草和农药化肥喷洒具有周期性的特点,农业车辆在执行农田作业时具有较强的重复性. 基于迭代学习控制(Iterative learning control,ILC)方法研究农业车辆的路径跟踪问题,建立了农业车辆的两轮移动机器人运动学模型,设计了车辆路径跟踪的迭代学习控制算法,并基于压缩 映射方法理论上证明了算法的收敛性. 研究表明,迭代学习控制可有效利用农业车辆运行的重复信息,实现车辆期望路径有限区间内的高精度完全跟踪控制. 仿真示例验证了本文方法的有效性. 相似文献
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论述了迭代学习控制的基本理论问题,着重讨论了迭代学习理论研究的现状及其存在的问题,提出了一些有待进一步研究的方 面。 相似文献
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迭代学习控制(ILC)作为智能控制的一个分支,经历了约二十年的发展,在理论研究和实际应用方面都取得了可喜的成果,在过程控制中逐渐显现其独到之处。本文在广泛介绍ILC算法的同时,系统地介绍了最近几年ILC理论研究现状、ILC存在的问题和研究方向。 相似文献
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介绍输出概率密度函数(PDF)常规的迭代学习控制(ILC)的收敛条件,并利用此条件设计相应的迭代学习律.主要讨论如何解决输出PDF迭代学习控制(ILC)中的过迭代,收敛速度等问题.以离散输出概率密度函教(PDF)控制模型为基础,介绍了直接迭代学习控制算法收敛的必要条件,提出自适应的迭代学习参数调节方法和避免过迭代的迭代结束条件,这些措施能够保证输出PDF的迭代控制收敛且具有较快的收敛速度.仿真结果表明,输出PDF的自适应迭代学习控制具有较快的收敛速度,而学习终止条件能很好地避免过迭代. 相似文献
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In recent years, more research in the control field has been in the area of self‐learning and adaptable systems, such as a robot that can teach itself to improve its performance. One of the more promising algorithms for self‐learning control systems is Iterative Learning Control (ILC), which is an algorithm capable of tracking a desired trajectory within a specified error limit. Conventional ILC algorithms have the problem of relatively slow convergence rate and adaptability. This paper suggests a novel approach by combining system identification techniques with the proposed ILC approach to overcome the aforementioned problems. The ensuing design procedure is explained and results are accrued from a number of simulation examples. A key point in the proposed scheme is the computation of gain matrices using the steepest descent approach. It has been found that the learning rule can be guaranteed to converge if certain conditions are satisfied. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society 相似文献
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提出线性离散时间系统基于Jacobi方法的迭代学习控制问题.通过构建线性迭代学习控制问题与线性方程组之间的联系,将Jacobi方法引入到迭代学习控制中,并由此构建得到迭代学习控制律.借助于矩阵运算,证明这种学习律能使得系统的输出跟踪误差经有限次迭代后为零.数值例子说明了算法的可适用性. 相似文献
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In this work, an initial state iterative learning control (ILC) approach is proposed for final state control of motion systems. ILC is applied to learn the desired initial states in the presence of system uncertainties. Four cases are considered where the initial position or speed is a manipulated variable and the final displacement or speed is a controlled variable. Since the control task is specified spatially in states, a state transformation is introduced such that the final state control problems are formulated in the phase plane to facilitate spatial ILC design and analysis. An illustrative example is provided to verify the validity of the proposed ILC algorithms. 相似文献
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Importance of batch processes has grown recently with the increasing economic competition that has pushed the manufacturing industries to pursue small quantity production of diverse high value-added products. Accordingly, systems engineering research on advanced control and optimization of batch processes has proliferated. In this paper, we examine the potentials of ‘iterative learning control (ILC)’ as a framework for industrial batch process control and optimization. First, various ILC rules are reviewed to provide a historical perspective. Next it is shown how the concept of ILC can be fused with model predictive control (MPC) to build an integrated end product and transient profile control technique for industrial chemical batch processes. Possible extensions and modifications of the technique are also presented along with some numerical illustrations. Finally, other related techniques are introduced to note the similarities and contemplate the opportunities for synergistic integration with the current ILC framework. 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 linear
ILC, 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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In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The iterative learning control (ILC) theory provides a suitable framework for derivations and analysis of ballistic control under learning process. To overcome the obstacles caused by uncertain gradient and redundant control input, we incorporate extra trials into iterative learning. With the help of trial results, proper control and updating direction can be determined. Then, iterative learning can be applied to ballistic control problem. Several initial state learning algorithms are studied for initial speed control, force control, as well as combined speed and angle control. In the end, shooting angle learning in the basketball shot process is simulated to verify the effectiveness of iterative learning methods in ballistic control problems. 相似文献