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。     

同步发电机励磁控制器设计

针对基于开环迭代学习控制的同步发电机励磁控制器存在稳定性差、易受干扰影响、不能完全跟踪期望轨迹的问题,设计了一种基于开闭环PID迭代学习控制与电力系统稳定器结合的同步发电机励磁控制器;在开环迭代学习控制的基础上,引入闭环反馈控制,将上一次的输入和输出误差及当前的输出误差作为当前的控制输入,从而构成开闭环PID型迭代学习控制;同时,利用电力系统稳定器提供的附加正阻尼信号来改善同步发电机励磁控制系统的电压稳定性和功角稳定性。仿真结果表明,该励磁控制器具有较强的鲁棒性,提高了系统稳定性。     

高阶P型学习控制的鲁棒性

通过线性化处理，本文将开闭环配合的高阶Ｐ型迭代学习控制律的适用范围推广到更一般的非线性动态系统。对于ＵＢＢ初始条件和周期干扰，以及渐进重复初始条件和干扰的情形，文中分别证明了学习过程的渐进有界和一致收敛性。仿真结果表明了这类控制器的鲁棒性能。     

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.     

非线性系统开闭环PID型迭代学习控制算法的鲁棒性

针对非线性时变系统的迭代学习控制问题提出了一种开闭环PID型迭代学习控制律,并证明了系统满足收敛条件时,具有开闭环PID型迭代学习律的一类非线性时变系统在动态过程存在干扰的情况下控制算法的鲁棒性问题.分析表明,系统在状态干扰、输出干扰和初态干扰有界的情况下跟踪误差有界收敛,在所有干扰渐近重复的情况下可以完全地跟踪给定的期望轨迹.     

网络控制系统中一种新形式的开闭环迭代学习控制

本文研究了网络控制系统中迭代学习控制方法, 借鉴网络控制系统的分层结构形式, 提出了一种远程开环本地闭环的迭代学习控制形式. 这种形式的学习律中, 只有开环学习律的离散数据通过网络传输, 对控制系统进行前馈补偿同时降低了网络丢包对系统的实时影响. 针对线性系统, 利用2–D系统的分析方法, 给出了网络控制中开闭环迭代学习控制的收敛性判别条件. 仿真和实验结果证实了所提方法的可行性和有效性.     

分层递阶迭代学习控制的研究与仿真

本文对分层递阶迭代学习控制算法进行了研究。研究表明,采用分层递阶迭代学习控制算法以后,控制器不仅可以用于开环稳定系统,也可以应用于开环不稳定系统。通过对人工髋关节模拟试验机位置系统的数学模型进行仿真应用后表明,无论是开环迭代学习控制,还是闭环迭代学习控制,都能使系统的输出渐近跟踪希望轨迹,但闭环学习控制效果要比开环学习控制要好。     

基于H∞方法的不确定系统迭代学习控制设计

针对不确定线性离散系统，研究了开闭环型的鲁棒迭代学习控制器。给出了控制器收敛的充分条件，根据此条件，将迭代学习控制的设计问题转化为H∞设计问题，提出了一种兼具反馈闭环控制与前馈学习控制的鲁棒迭代学习控制律，并采用H∞性能指标对系统进行优化，使系统的收敛率基于H∞最优，然后使用线性不等式（LMI）方法求解迭代学习控制器的参数。仿真实例表明了该设计方法的有效性。     

迭代学习控制与二维分析

本文探讨了在学习时间有限和元情形下用二维系统方法分析迭代学习控制的可能性，给出线性离散系统在开环与闭环学习中各种学习律下的稳定与收敛的充要条件，并比较了各种学习律的收敛速度，指出随着学习次数的增加，在一定条件下，Ｐ型学习与其它类型的学习算法具有相同的收敛速度。     

迭代学习控制在大惯性系统中的应用

迭代学习控制是应用在具有重复运动特点的系统中,并有良好的跟踪效果的控制方法.本文针对转台控制系统中的大惯性环节,运用开环P型迭代学习控制,仿真证明该学习控制律的有效性.并证明学习控制律的收敛条件.     

一类输出饱和系统的学习控制算法研究

传感器饱和是控制系统中较为常见的一种物理约束. 本文针对一类含饱和输出的受限系统, 提出了两种学习控制算法. 具体而言, 首先, 对于重复运行的被控系统, 设计了开环P型迭代学习控制器, 实现在有限时间区间内对期望轨迹的完全跟踪, 并在λ范数意义下分析了算法的收敛性, 给出了含饱和输出的迭代学习控制系统的收敛条件. 进而, 针对期望轨迹为周期信号的被控系统, 提出了闭环P型重复学习控制算法, 并分析了这类系统的收敛性条件. 最后, 通过一个仿真实例验证了本文所提算法的有效性.     

On control relevant criteria in H∞ identification

This paper proposes a technique for using control relevant criteria in H_∞ identification. The work reported here has its background in a desire to understand the closed-loop versus open-loop issue in control relevant identification. The proposed technique has some features in common with the iterative closed-loop Schrama scheme, but is constructed so as to be able to obtain control relevant reduced complexity models also directly from open-loop data (for stable systems). It is demonstrated that the proposed technique solves, with the initial open-loop data only, the examples treated earlier in the literature using the iterative closed-loop Schrama scheme     

Iterative learning controllers with time-varying gains for large-scale industrial processes to track trajectories with different magnitudes

In this article, a set of decentralised open-loop and closed-loop iterative learning controllers are embedded into the procedure of steady-state hierarchical optimisation utilising feedback information for large-scale industrial processes. The task of the learning controllers is to generate a sequence of upgraded control inputs iteratively to take responsibility for sequential step function-type control decisions, each of which is determined by the steady-state optimisation layer and then imposed on the real system for feedback information. In the learning control scheme, the learning gains are designated to be time-varying which are adjusted by virtue of expertise experiences-based IF-THEN rules, and the magnitudes of the learning control inputs are amplified by the sequential step function-type control decisions. The aim of learning schemes is to further effectively improve the transient performance. The convergence of the updating laws is deduced in the sense of Lebesgue 1-norm by taking advantage of the Hausdorff–Young inequality of convolution integral and the Hoelder inequality of Lebesgue norm. Numerical simulations manifest that both the open-loop and the closed-loop time-varying learning gain-based schemes can effectively decrease the overshoot, accelerate the rising speed and shorten the settling time, etc.     

On the P-type and Newton-type ILC schemes for dynamic systems with non-affine-in-input factors

In this paper, P-type learning scheme and Newton-type learning scheme are proposed for quite general nonlinear dynamic systems with non-affine-in-input factors. Using the contraction mapping method, it is shown that both schemes can achieve asymptotic convergence along learning repetition horizon. In order to quantify and evaluate the learning performance, new indices—Q-factor and Q-order—are introduced in particular to evaluate the learning convergence speed. It is shown that the P-type iterative learning scheme has a linear convergence order with limited learning convergence speed under system uncertainties. On the other hand, if more of system information such as the input Jacobian is available, Newton-type iterative learning scheme, which is originated from numerical analysis, can greatly speed up the learning convergence speed. The effectiveness of the two learning control methods are demonstrated through a switched reluctance motor system.     

非线性系统迭代学习算法

对于一个未知的非线性连续系统或离散系统,从任给的一个初始控制出发,尝试实现一条 给定的输出目标轨线.在满足一定条件下,利用跟踪误差来修正控制函数,经过反复的迭代学 习可以取得满意的效果.本文改进了Arimoto、Togai和Bien等的开环迭代学习的收敛条 件,并提出闭环迭代学习算法.理论与仿真结果证明了闭环算法在收敛条件、速度和抗干扰能 力上都优于开环算法.     

Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators

In this paper, an open-loop PD-type iterative learning control (ILC) scheme is first proposed for two kinds of distributed parameter systems (DPSs) which are described by parabolic partial differential equations using non-collocated sensors and actuators. Then, a closed-loop PD-type ILC algorithm is extended to a class of distributed parameter systems with a non-collocated single sensor and m actuators when the initial states of the system exist some errors. Under some given assumptions, the convergence conditions of output errors for the systems can be obtained. Finally, one numerical example for a distributed parameter system with a single sensor and two actuators is presented to illustrate the effectiveness of the proposed ILC schemes.      

On D-type and P-type ILC designs and anticipatory approach

Many schemes of iterative learning control (ILC) have been developed for continuous-time, non-linear dynamic systems to improve tracking performance. Two schemes, D-type and P-type, have been the bases for many ILC designs. Recently, the anticipatory ILC scheme has been introduced, on the basis of a different approach (Wang 1998a, 1999). In this paper, these basic schemes are compared from both analysis and implementation view points. The anticipatory ILC scheme is designed on the basis of a causal pair of the action taken and its resulting state variables. This approach has the anticipatory characteristics of the D-type ILC and the simplicity for implementation of P-type ILCs. A sampled-data ILC scheme is presented as another form of this anticipatory ILC scheme. Furthermore, control device saturation is taken into account and tracking error convergence results are established, with proofs. The convergence results are also provided in the presence of uncertainties, disturbances and measurement noises. Experimental results are presented to show the effectiveness of this scheme.     

非线性抛物型偏差分系统迭代学习控制

本文研究了一类时空离散的非线性抛物型偏差分系统的迭代学习控制问题.首先,针对系统含有不确定系数与非线性特点,设计了开环P型迭代学习控制算法;然后,建立了输出跟踪误差沿迭代轴收敛的充分条件,并利用离散Gronwall不等式、λ范数以及压缩映射原理,详细给出了收敛性分析证明.最后通过仿真实例说明了算法的有效性.