高相对阶连续时间系统的间接迭代学习控制

本文提出了一类高相对阶线性连续时间系统的间接迭代学习控制算法,该算法相对独立于系统局部控制器,因此可以应用于已有局部反馈控制器的系统.采用具有极点配置的H_∞鲁棒控制器作为系统的内环控制,而在外环通过迭代学习控制调整内环系统的指令信号.通过引入拉氏变化,构建了迭代学习系统的2-D Roesser模型,推导了系统渐近收敛条件,并研究了存在有界初始条件偏移和迭代变化外部干扰时算法的鲁棒性能.最后,利用空中加油对接控制的算例进一步验证了算法的有效性.     

离散非线性系统开闭环P型迭代学习控制律及其收敛性

本文在讨论了一般开环与闭环迭代学习控制的不足后，针对一类离散非线性系统，提出了新的开闭环ＰＧ型迭代学习控制律，给出了它的收敛性证明，仿真结果表明：开闭环Ｐ型迭代律优于单纯的开环或产才环Ｐ型迭代 律。     

基于迭代学习控制算法的网络机器人控制系统

针对网络机器人控制系统中存在的干扰等不确定性因素,首先对网络机器人控制系统和迭代学习控制理论进行研究。在此基础上,利用迭代学习控制不依赖于动态系统的精确数学模型等优点,将其运用到网络机器人控制系统中,在同时考虑其存在干扰的情况下,给出了系统模型及详细的算法设计过程。通过Matlab平台进行仿真,表明了该算法的有效性。     

非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。开环迭代学习控制学习周期长，在迭代学习的初期容易出现不稳定和高增益的现象。对非正则系统的迭代学习控制，需要采用高阶微分学习律。该文针对一类非正则线性定常连续系统，讨论了闭环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，解决了非正则系统的P型迭代学习控制问题。仿真实例说明闭环迭代学习律的有效性和快速性。     

非线性系统闭环P型迭代学习控制的收敛性

本文得到并证明了当被控系统的状态方程为一类非线性方程时，采用闭环Ｐ型学习律迭代学习控制的收敛的充分条件和必要条件，最后，我们给出了典型的仿真结果。     

非线性系统开闭环PI型迭代学习控制律及其收敛性

对于一类参数未知的非线性系统在有奶时域上的精确轨迹跟踪问题，提出了一种开闭环ＰＩ型迭代学习控制策略，给出了其收敛的充要条件，分析表明：所给出的收敛条件推广了现有结果。     

倒立摆的迭代学习控制方法研究

针对倒立摆系统典型的非线性、多变量和自然不稳定的特点,在建立系统数学模型基础上,利用迭代学习控制方法,根据动态指标的要求设计了控制器,并完成了系统仿真,结果表明:与传统反馈控制相比较,所设计的迭代学习控制器使系统的动态性能得到了很大的改善。     

非线性时变系统开闭环P型迭代学习控制的收敛性

对于非线性时变系统,给出了其开闭环P型迭代学习控制收敛的充要条件.这些收敛条件与被控系统状态方程的具体形式无关.对比表明,该文的结论改进了现有结果.     

闭环网络控制系统研究综述

系统地介绍了目前闭环网络控制系统的研究现状 .基于信息传输延迟的随机时变特性 ,对对象建模、闭环网络系统的确定性控制以及随机控制等各种控制策略进行了论述 .并在此基础上 ,分析了闭环网络控制系统设计中尚待解决的问题以及一些新的研究方向     

离散非线性时变系统开闭环PI型迭代学习控制律及其收敛性

对于具有重复运动性质的对象,迭代学习控制是一种有效的控制方法.针对一类离散非线性时变系统在有限时域上的精确轨迹跟踪问题,提出了一种开闭环PI型迭代学习控制律.这种迭代律同时利用系统当前的跟踪误差和前次迭代控制的跟踪误差修正控制作用.给出了所提出的学习控制律收敛的充分必要条件,并采用归纳法进行了证明.最后用仿真结果对收敛条件进行了验证.     

An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.     

An iterative learning control design approach for networked control systems with data dropouts

In network‐based iterative learning control (ILC) systems, data dropout often occurs during data packet transfers from the remote plant to the ILC controller. This paper considers the problem of controller design for such ILC processes. Packet missing is modeled by stochastic variables satisfying the Bernoulli random binary distribution, which renders such an ILC system to be a stochastic one. Then, the design of ILC law is transformed into the stabilization of a 2‐D stochastic system described by the Roesser model. A sufficient condition for mean‐square asymptotic stability is established by means of a linear matrix inequality technique, and formulas can be given for the control law design simultaneously. This result is further extended to more general cases where the system matrices also contain uncertain parameters. The effectiveness and merits of the proposed method are illustrated by a numerical example. Copyright © 2015 John Wiley & Sons, Ltd.     

Convergence properties of two networked iterative learning control schemes for discrete-time systems with random packet dropout

This paper addresses convergence issue of two networked iterative learning control (NILC) schemes for a class of discrete-time nonlinear systems with random packet dropout occurred in input and output channels and modelled as 0–1 Bernoulli-type random variable. In the two NILC schemes, the dropped control input of the current iteration is substituted by the synchronous input used at the previous iteration, whilst for the dropped system output, the first replacement strategy is to replace it by the synchronous pre-given desired trajectory and the second one is to substitute it by the synchronous output used at the previous iteration. By the stochastic analysis technique, we analyse the convergence properties of two NILC schemes. It is shown that under appropriate constraints on learning gain and packet dropout probabilities, the tracking errors driven by the two schemes are convergent to zero in the expectation sense along iteration direction, respectively. Finally, illustrative simulations are carried out to manifest the validity and effectiveness of the results.     

Use of neural networks in iterative learning control systems

This paper presents an approach to the use of neural networks to improve iterative learning control performance. The neural networks are used to estimate the learning gain of an iterative learning law and to store the learned control input profiles for different reference trajectories. A neural network of piecewise linear approximation is presented to identify effectively the system dynamics, and the approximation property and persistently exciting condition are discussed. In addition, training of a feedforward neural controller is presented to accumulate control information learned by an iterative update law for various reference trajectories. Then, an iterative learning law with a feedforward neural controller is suggested and its convergence property is stated with the convergence condition. The effectiveness of the present methods has been demonstrated through simulations by applying them to a two-link robot manipulator.     

寻找多智能体系统一致性的迭代学习方法

本文利用迭代学习的方法研究了带头结点的多智能体系统的一致性问题.文中分别对单积分多智能体系统和一般的线性多智能体系统提出了迭代学习型的一致性算法.该算法对每一个从节点所设计的分布迭代学习序列可以保证从节点能完全跟随上头结点.假设头结点是全局可达的,对于有向拓扑连接图,给出了智能体达到完全一致的充分条件.最后,仿真实例说明了文中所给方法的有效性.     

Parameter identification of continuous-time systems using iterative learning control

This paper presents a system identification technique for continuous-time state-space system using the iterative learning control. The transfer function parameters are regarded as functions with respect to the state-space parameters which will be identified. The relationship between the state-space parameters and the response error is explicitly derived. An update law of the state-space parameters is proposed so as to improve the convergence speed. The effectiveness of the proposed identification technique is demonstrated by numerical examples.     

不确定性机器人系统自适应鲁棒迭代学习控制

利用Lyapunov方法, 提出了一种不确定性机器人系统的自适应鲁棒迭代学习控制策略, 整个系统在迭代域里是全局渐近稳定的. 所考虑的机器人系统同时包含了结构和非结构不确定性. 在设计时, 系统的不确定性被分解成可重复性和非重复性两部分, 并考虑了系统的标称模型. 在所提出的控制策略中, 自适应策略用来估算做法确定性的界, 界的修正与迭代学习控制量一样的迭代域得以实现的. 计算机仿真表明本文提出的控制策略是有效的.     

Set-point-related indirect iterative learning control for multi-input multi-output systems

A form of iterative learning control (ILC) is used to update the set-point for the local controller. It is referred to as set-point-related (SPR) indirect ILC. SPR indirect ILC has shown excellent performance: as a supervision module for the local controller, ILC can improve the tracking performance of the closed-loop system along the batch direction. In this study, an ILC-based P-type controller is proposed for multi-input multi-output (MIMO) linear batch processes, where a P-type controller is used to design the control signal directly and an ILC module is used to update the set-point for the P-type controller. Under the proposed ILC-based P-type controller, the closed-loop system can be transformed to a 2-dimensional (2D) Roesser s system. Based on the 2D system framework, a sufficient condition for asymptotic stability of the closed-loop system is derived in this paper. In terms of the average tracking error (ATE), the closed-loop control performance under the proposed algorithm can be improved from batch to batch, even though there are repetitive disturbances. A numerical example is used to validate the proposed results.     

Adaptive iterative learning control for switched nonlinear continuous-time systems

In this paper, an adaptive iterative learning control (ILC) method is proposed for switched nonlinear continuous-time systems with time-varying parametric uncertainties. First, an iterative learning controller is constructed with a state feedback term in the time domain and an adaptive learning term in the iteration domain. Then a switched nonlinear continuous-discrete two-dimensional (2D) system is built to describe the adaptive ILC system. Multiple 2D Lyapunov functions-based analysis ensures that the 2D system is exponentially stable, and the tracking error will converge to zero in the iteration domain. The design method of the iterative learning controller is obtained by solving a linear matrix inequality. Finally, the efficacy of the proposed controller is demonstrated by the simulation results.     

输出重定义下的非线性非最小相位系统迭代学习控制

讨论非线性非最小相位系统实现完全跟踪的迭代学习控制方法, 适于在有限作业区间上重复运行的受控系统. 在控制器设计时, 通过输出重定义以使非最小相位系统的零动态变成渐近稳定特性. 分别采用部分限幅和完全限幅两种学习算法设计控制器, 理论分析表明两种算法能够保证学习系统中所有变量的有界性和跟踪误差在整个作业区间上渐近收敛于零. 数值仿真验证了两种迭代学习控制系统的跟踪性能.