Robust design of integrated feedback and iterative learning control of a batch process based on a 2D Roesser system

To improve stability and convergence, feedback control is often incorporated with iterative learning control (ILC), resulting in feedback feed-forward ILC (FFILC). In this paper, a general form of FFILC is studied, comprising of two feedback controllers, a state feedback controller and a tracking error compensator, for the robustness and convergence along time direction, and an ILC for performance along the cycle direction. The integrated design of this FFILC scheme is transformed into a robust control problem of an uncertain 2D Roesser system. To describe the stability and convergence quantitatively along the time and the cycle direction, the concepts of robust stability and convergence along the two axes are introduced. A series of algorithms are established for the FFILC design. These algorithms allow the designer to balance and choose optimization objectives to meet the FFILC performance requirements. The applications to injection molding velocity control show the good effectiveness and feasibility of the proposed design methods.     

ILC教学设计初探

本文通过对ILC教学理念、教学策略、教学设计以及教学评价的研究,探讨科学有效的ILC教学设计模式.     

基于迭代学习的开关磁阻电动机最优控制研究

以转矩脉动最小化为目标,提出了一种开关磁阻电动机（SRM）最优控制器的设计方法.基于迭代学习控制（ILC）的基本原理,阐述了单输入单输出（SISO）非线性系统中ILC的收敛性和稳定性的一般性结论.建立了以非线性磁链模型为基础的SRM数学模型.所设计的最优控制器由基于转矩分配函数（TSF）的转矩控制器和电流控制器构成,转矩控制器根据期望转矩经过“学习”得出期望电流曲线,电流控制器经过“学习”使实际输出电流逼近期望电流曲线,从而实现转矩控制.仿真结果表明,在恒转矩负载下,转矩脉动减小,且具有较好的收敛性与稳定性,达到了转矩脉动最小的优化目的.     

On iterative learning control with high‐order internal models

In this work we focus on iterative learning control (ILC) for iteratively varying reference trajectories, which are described by a high‐order internal models (HOIM) that can be formulated as a polynomials between two consecutive iterations. The classical ILC with iteratively invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a first‐order internal model with a unity coefficient. By incorporating HOIM into the ILC law, and designing appropriate learning control gains, the learning convergence in the iteration axis can be guaranteed for continuous‐time linear time‐varying systems. The initial resetting condition, P‐type and D‐type ILC, and possible extension to nonlinear cases are also explored in this work. Copyright © 2010 John Wiley & Sons, Ltd.     

柴油发电机调速系统迭代学习控制

针对柴油发电机调速系统大多采用传统PID控制,提出采用以PID作为学习律的迭代学习控制(ILC)策略,利用其具有不依赖于被控系统的精确数学建模,跟踪收敛快的特性,设计了柴油发电机调速系统控制器。在MATLAB/Simulink中建立了柴油发电机调速系统仿真模型,对3种基本工况进行了仿真研究,详细分析了ILC在柴油发电机调速控制过程中的跟踪特性,并将控制效果和传统PID控制进行了对比,从仿真结果发现,基于ILC建立的控制器不仅适用于柴油发电机调速系统,而且表现出更好的动态性能。为柴油发电机的控制提供了新途径。     

电力系统暂态稳定问题和迭代学习控制的研究

提出暂态稳定是解决电力系统稳定问题的重点,由于电力系统的非线性,模型和干扰 的不确定,寻找较少依赖于数学模型、具有较强适应性的控制策略是电力系统稳定控制的 重要课题。例如,在特定的条件下,重要的FACTS装置STATCOM如控制不当,则有可能出现负 阻尼现 象,诱发系统振荡。将迭代学习控制方法应用到FACTS装置的阻尼振荡控制中,对系统的各 种运行点具有很好的适应性,并可自动消除负阻尼的出现。同时证明了这种控制方法用于两 种FACTS装置的收敛性。仿真结果表明迭代学习控制法与常规PI控制相比,在很多情况下具 有较好的适应性。     

基于终点时刻末端误差的柔性臂迭代学习控制

针对柔性臂重复运行的情况,在仅能测量运行终点时刻末端位置的条件下,提出一种新的结合计算力矩法的迭代学习控制（ILC）方法.该方法利用柔性臂的简化动力学模型,给出各关节控制力矩的参数化表示;并依据终点时刻柔性臂末端位置的误差,通过迭代学习算法调整控制力矩的参数,实现精确到达预期末端位置的目标.算法利用ILC不依赖模型的特点,弥补计算力矩法需要精确模型的缺陷;参数的迭代学习主要起到消除模型误差和各种干扰的作用,增强算法的鲁棒性.通过理论分析给出所提算法的收敛条件.最后在柔性臂系统上进行仿真及实际试验.结果表明,所提出的ILC算法能够克服连杆柔性对柔性臂末端误差的影响,显示良好的控制效果.