非线性离散时间系统的最优终端迭代学习控制

仅利用系统的终端输出误差而不是整个输出轨迹,提出了一种最优终端迭代学习控制方法.控制信号可直接通过终点的误差信息进行更新.主要创新点在于控制器的设计和分析只利用系统量测的I/O数据而不需要关于系统模型的任何信息,并可实现沿迭代轴的单调收敛.在此意义上,所提出的控制器是数据驱动的无模型控制方法.严格的数学分析和仿真结果均表明了所提出方法的适用性和有效性.     

基于ILC&EKF的PM SM转矩脉动最小化方法的仿真研究

提出一种新型的将频域中的迭代学习控制算法(ILC)和扩展卡尔曼滤波器(EKF)相结合来抑制永磁同步电动机(PMSM)转矩脉动的方法。迭代学习算法用来抑制电机运行过程中产生的周期性的转矩脉动。由于在电机终端安装传感器会提高系统的成本、引入噪声等,提出使用扩展卡尔曼滤波器作为观测器来估计系统参数,从电机输出终端根据电机的定转子的电流和电压值准确估计出电机的速度值、转矩值和转子位置角的大小。通过matlab/simulink工具箱仿真结果证明了该方法的可行性和有用性。     

Single-cycle and multi-cycle generalized 2D model predictive iterative learning control (2D-GPILC) schemes for batch processes

In this paper, iterative learning control (ILC) system is modeled and designed from a two-dimensional (2D) system point of view. Based on a 2D cost function defined over a single-cycle or multi-cycle prediction horizon, two ILC schemes, referred respectively as single-cycle and multi-cycle generalized 2D predictive ILC (2D-GPILC) schemes, have been proposed and formulated in the GPC framework for the 2D system. Analysis shows that the resulted control schemes are the combination of a time-wise GPC and a cycle-wise ILC optimized in 2D sense. Guidelines for parameter tuning have been proposed based on the ultimate performance analysis for the control system. Simulation shows that the multi-cycle 2D-GPILC outperforms the single-cycle 2D-GPILC in term of cycle-wise convergence.     

正则线性系统的闭环P型迭代学习控制

迭代学习控制是改善具有重复运行性质过程的跟踪性能的有效方法.针对一类正则线性定常连续系统的两种情形,提出了闭环P型迭代学习控制律,给出并证明了闭环P型迭代学习控制律的收敛性条件的两个定理.仿真实例说明,该迭代学习律的有效性.     

Delay-range-dependent robust 2D iterative learning control for batch processes with state delay and uncertainties

This paper proposes the design of the integrated output feedback and iterative learning control (ILC) for batch processes with uncertain perturbations and interval time-varying delays, where the main idea is to transform the design into a robust delay-range-dependent H_∞ control of a 2D system described by a state-space model with varying delays. A sufficient criterion for delay-dependent H_∞ noise attenuation is derived through linear matrix inequality (LMI) by introducing a comprehensive 2D difference Lyapunov–Krasovskii functional candidate and adding a differential inequality to the difference in the Lyapunov function for the 2D system. Based on the criterion obtained, the delay-range-dependent output feedback controller combined with ILC is then developed. The developed system ensures that the closed-loop system for all admissible uncertainties is asymptotically stable and has a prescribed H_∞ performance level in terms of the LMI constraint. The controller is obtained by solving an LMI optimization problem with simple calculations and less constraint conditions. Moreover, the conditions can also be directly extended from delay-range-dependent to general delay-dependent stability. Applications in injection velocity control demonstrate the effectiveness and feasibility of the proposed method.     

Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions

This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme.     

同步发电机初态学习与PSS结合的励磁控制

为了解决开环迭代学习励磁控制器对大干扰耐受力差、对初始定位要求严格的问题,针对多机电力系统,设计了具有初态学习的开闭环迭代学习控制(ILC)+PSS的励磁控制器。该控制器在开环ILC的基础上,引入机端电压闭环反馈信号,另外为放宽对初态的要求,允许有一定的初始定位误差,对算法还进一步改进,初态也进行学习,同时还结合PSS提供的正阻尼能力,以增强系统的功角稳定性。最后,仿真结果表明,该励磁控制器不但能快速达到机端电压的调节精度,而且改善了系统的功角稳定性。     

交直流混合微电网分段协调控制策略

为解决交直流混合微电网因微源出力变化、负荷变化和储能装置因荷电状态导致充放电功率发生变化等引起的功率波动问题,提出一种混合微电网分段协调控制策略。针对孤岛状态下的混合微电网,分析了混合微电网的典型拓扑和各运行模式下的功率关系。采用标幺化的方法得到了可表征混合微电网整体运行状态的特征量,根据该特征量的变化量对控制策略进行分段,对各段控制的工作原理进行了详细的分析,具体研究了在不同控制段中各变流器的相互协调;针对可能出现的网间交换功率振荡以及互联变流器(ILC)运行模式频繁切换的问题,对动作判据进行了补偿。最后,在PSCAD/EMTDC中建立了仿真模型,仿真结果表明,在不同工况下各变流器均可迅速做出响应,保证系统的稳定运行。     

一类含控制时滞非线性系统在任意初值下的开环PD型迭代学习控制

在迭代学习控制理论的收敛性分析中,常见的初始条件是迭代初值与期望初值一致,或者迭代初值固定,给出了一类含控制时滞非线性时变系统在任意初值条件下采用开环PD型迭代学习控制算法时的收敛条件。迭代学习采用控制输入与初值同时学习的算法,其中控制输入利用了给定超前法,该算法解决了控制时滞和初值问题。运用算子理论证明了收敛条件,给出了间歇非线性控制时滞过程仿真实例,研究结果说明了该算法的有效性。     

知识继承型迭代学习控制的研究与应用

针对一类具有同质特征的多维轨迹群,提出基于知识继承的迭代学习控制（ILC）策略. 该策略以一类工业机器人系统为控制对象,在跟踪具有渐变幅值的同质轨迹群（HTG）时,应用迭代学习控制方法,从起始源轨迹中获得基准控制知识. 将基准控制知识预设为下一新轨迹迭代学习的首次运行知识. 通过增益变换和偏移变换实现迭代学习控制的知识继承,使得该类工业机器人系统加快对新轨迹的学习速度,以此降低跟踪同质轨迹群的整体学习次数,实现跟踪效率的较大提升. 理论分析和仿真结果证明了所提控制策略的优越性.