基于有限时间跟踪微分器的迭代学习控制

针对迭代学习控制（Iterative learning control,ILC）中的初始状态问题,提出了采用有限时间跟踪微分器安排过渡过程方法,根据迭代学习控制中期望轨迹已知的特点,设计了其参数有明显物理意义并且调节方便的有限时间跟踪微分器. 在此基础上,针对一类具有不确定性的非线性时变系统的迭代学习控制问题,提出了具有对不确定项进行估计的迭代学习控制算法,并应用类Lyapunov方法给出了相关定理证明. 仿真结果表明所提出的方法是有效的.     

基于迭代学习控制的混合误差轮廓控制

针对永磁直线同步电动机直接驱动XY平台系统中存在不确定扰动的问题,单轴上设计了基于干扰观测器的迭代学习控制器.迭代学习控制器可以抑制重复扰动,干扰观测器可以消除非重复扰动的影响,两者结合起来可增强系统对重复和非重复扰动的抑制能力.由于轮廓误差计算模型要求跟踪误差远小于期望轮廓曲率半径,所以设计了混合误差轮廓控制器,跟踪误差较大时只控制各轴位置,跟踪误差足够小时进行轮廓控制.仿真结果表明,该控制方法使直接驱动XY平台具有较强的鲁棒性和较高的轮廓精度.     

Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Channel Noise

Channel noise, including sensor‐to‐controller(SC) noise and controller‐to‐actuator(CA) noise, impacts the convergence of wireless remote iterative learning control (ILC) system significantly. In this paper, the relationship between output error, SC noise and CA noise is obtained firstly by super‐vector formulation, and then the norm of output error vector covariance matrix is employed to analyze the convergence of the system in presence of SC noise and CA noise. Upper bound of the norm at any sample time reveals that the SC noise is accumulated only in iteration domain, while the CA noise is accumulated not only in iteration domain but also in time domain. Furthermore, the accumulated effect of the CA noise in time domain is ruled by system matrices, so the values of which determine the effect of the CA noise is greater or less than that of the SC noise on convergence of the system. Finally, some simulation results are given to illustrate correctness of the result.     

带有迭代学习前馈的快速路无模型自适应入口匝道控制

提出了一种新的带有迭代学习前馈的快速路无模型自适应入口匝道控制算法. 模块化的前馈迭代学习和反馈MFAC控制器设计方案使所设计的控制系统有效地利用了交通流的周期性特征, 提高了控制品质. 严格的数学推导证明了该方法的收敛性. 仿真研究及比较结果验证了所提算法的有效性.     

高频角振动测试转台的迭代学习控制

为了提高角振动转台在陀螺高频特性测试时对周期性期望轨迹的跟踪精度,该文在PID型迭代学习控制的基础上,针对转台控制系统的稳定性和迭代学习的收敛性进行解耦设计,得到一种复合迭代学习控制器,并从频域角度给出了其收敛性条件,最后用MatlabSimulink工具箱对该设计进行了仿真研究.仿真结果表明,与常规比例-积分-微分控制相比较,这种设计改善了系统输出跟踪周期性正弦信号输入的精度,提高了系统带宽,并对系统消除周期性干扰有所裨益.结论表明这种复合迭代控制器可以应用于高频角振动转台的控制.     

Comments on “Robust optimal design and convergence properties analysis of iterative learning control approaches” and “On the P-type and Newton-type ILC schemes for dynamic systems with non-affine input factors”

The papers by Xu and Tan [Robust optimal design and convergence properties analysis of iterative learning control approaches, Automatica 38 (2002) 1867-1880], and Xu and Tan [On the P-type and Newton-type ILC schemes for dynamic systems with non-affine input factors, Automatica 38 (2002) 1237-1242], give a convergence analysis for several iterative learning control approaches. Unfortunately, these papers contains several mathematical errors that render the proofs of the claimed results invalid. As there are no obvious ways to correct these errors, the results presented in these papers are questionable.     

ILC beam energy measurement based on synchrotron radiation from a magnetic spectrometer

We propose to measure, on a bunch-to-bunch basis, the beam energy at the International Linear e⁺e^- Collider by recording synchrotron radiation (SR) light emitted in the magnets of an energy spectrometer based on beam position monitors. Measuring the width of the horizontal SR fan permits to determine the relative beam energy with a precision better than 10^-4. There are two different measuring schemes possible. The first one is based on edge measurements of the direct SR fan, while the second option includes mirrors to deflect soft SR light to detectors located sufficiently off the beamline. Three possibilities for high-spatial resolution detectors are considered: a standard silicon strip detector, a novel-type Si detector and a gas amplification detector, both with exceptional position resolution. The main issue of the first scheme is the high radiation dose expected in the direct SR fan. If mirrors are used this dose is strongly reduced and allows application of any of the three detectors proposed.     

HIGH-ORDER ILC WITH INITIAL STATE LEARNING FOR DISCRETE-TIME DELAYED SYSTEMS

This article addresses an iterative learning control (ILC) design for a class of linear discrete-time systems with multiple time delays. In order to improve the tracking performance, we introduce a P-type high-order iterative learning algorithm that makes use of information from several previous iterations. An initial state learning scheme is proposed to eliminate the effect of the initialization error on the final tracking error. Furthermore, we establish a sufficient condition to ensure asymptotic convergence. A simulation example is also provided to illustrate the effectiveness of the proposed result.