Fundamental Trackability Problems for Iterative Learning Control

Generally, the classic iterative learning control(ILC)methods focus on finding design conditions for repetitive systems to achieve the perfect tracking of any specified trajectory,whereas they ignore a fundamental problem of ILC: whether the specified trajectory is trackable, or equivalently, whether there exist some inputs for the repetitive systems under consideration to generate the specified trajectory? The current paper contributes to dealing with this problem. Not only is a concept of trackability introduced formally for any specified trajectory in ILC, but also some related trackability criteria are established. Further, the relation between the trackability and the perfect tracking tasks for ILC is bridged, based on which a new convergence analysis approach is developed for ILC by leveraging properties of a functional Cauchy sequence(FCS). Simulation examples are given to verify the effectiveness of the presented trackability criteria and FCS-induced convergence analysis method for ILC.

基于控制器动态线性化的数据驱动ILC

针对未知非线性非仿射重复离散时间系统,将迭代域的动态线性化技术应用于非线性被控系统和未知非线性理想学习控制器,提出一种新的数据驱动迭代学习控制方案。通过设计一种改进的无模型自适应迭代学习控制算法对理想学习控制器的增益进行估计,该方案仅利用非线性系统的输入输出数据,便可自动调整迭代学习控制律。数值仿真和对高速列车模型的仿真验证了该方法的有效性和适用性。

基于高阶内模的变参考轨迹鲁棒迭代学习控制

针对一类带有非重复不确定参数的线性离散系统跟踪批次变化的参考轨迹问题,提出一种双环控制结构的鲁棒间接型迭代学习控制(ILC)算法。通过在内环部分设计比例-积分(PI)型反馈控制器保证闭环系统沿时间轴方向的稳定性,实现前几批次对参考轨迹的快速跟踪。在外环部分,通过高阶内模(HOIM)描述参考轨迹变化规律,并设计一个基于内模原理的高阶比例(P)型ILC控制器提升系统在批次方向上对变参考轨迹的跟踪性能,实现对变化的参考轨迹精确跟踪。针对不确定性参数带来的扰动问题,设计一类性能指标函数,将系统模型在间接型ILC控制器作用下转换为等价的重复过程模型;基于重复过程模型稳定性理论,将保证系统具有沿批次鲁棒稳定的性能指标条件转换为线性矩阵不等式(LMI)。最后通过一类永磁电机的控制仿真验证了所提算法的有效性。

可控串联补偿器暂态稳定的迭代自学习控制(ILC)

介绍了迭代自学习控制的原理 ,将其用于 FACTS(以可控串补为例 )的暂态稳定控制 ;比较了固定串补、PI控制和迭代自学习控制的可控串补对系统暂态稳定的影响 ;指出迭代自学习控制具有一系列常规控制算法所没有的优点 。

基于功能性电刺激的肘部运动迭代学习控制研究

功能性电刺激(functional electrical stimulation,FES)是治疗脑卒中后运动障碍的重要方法。考虑到现有动力学建模方法复杂、控制精度有待提高,该文提出了一种基于FES的肘部运动的带有遗忘因子的迭代学习控制方法。该研究利用Hammerstein结构建立肘部肌肉骨骼模型,在此基础上设计了带有遗忘因子的迭代学习控制的FES系统。通过计算样本实际输出与模型输出角度之间的均方根误差和最大角度误差,检验所建立的肌骨模型的可靠性。通过仿真分析和实际控制实验,评估了闭环FES系统的控制效果,并与传统的迭代学习控制FES系统进行了对比,说明了控制方法的有效性。研究结果表明,所建立的肘部肌骨模型适用于研究电刺激下肘关节的运动特性,并且带有遗忘因子的迭代学习控制方法在肘部控制中表现出更优越的性能。

Stability of Iterative Learning Control with Data Dropouts via Asynchronous Dynamical System

In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.

Robust iterative learning control for nonlinear systems with measurement disturbances

The iterative learning control （ILC） has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.

A New Discrete-time Adaptive ILC for Nonlinear Systems with Time-varying Parametric Uncertainties

用在分离时间轴和反复的学习轴之间的类比，一条新分离时间的适应反复的学习控制(AILC ) 途径被开发与变化时间的参量的无常探讨非线性的系统的一个班。类似于适应控制，新 AILC 能合并一个设计算法，因此，学习获得能沿着学习的轴反复地被调节。当起始的状态是随机的，参考轨道是变化重复的时，新 AILC 能沿着反复的学习轴 asymptotically 在有限时间间隔上完成 pointwise 集中。

Error analysis for remote nonlinear iterative learning control system with wireless channel noise

In this paper, the iterative learning control problem is considered for a class of remote control system over wireless network communication channel. The control performance of remote iterative learning control （R-ILC） system is analyzed and an error boundary of the stable output of the R-ILC system is obtained for the boundary stochastic noise channel. Finally, we obtain some rules to reduce the fluctuation caused by wireless channel noise through the analysis results for fluctuation boundary. The simulation results prove the proposed rule is correct.

Adaptive Iterative Learning Control for Nonlinear Time-delay Systems with Periodic Disturbances Using FSE-neural Network

An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion （FSE） are combined into a new function approximator to model each suitable disturbed function in systems. The requirement of the traditional iterative learning control algorithm on the nonlinear functions （such as global Lipschitz condition） is relaxed. Furthermore, by using appropriate Lyapunov-Krasovskii functionals, all signs in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded, and the output of the system is proved to converge to the desired trajectory. A simulation example is provided to illustrate the effectiveness of the control scheme.

基于ILC算法的飞机主动侧杆位置控制研究

针对飞机主动侧杆系统中现有比例控制算法的低精度问题,提出了一种结合比例控制与迭代学习控制的算法,该方法可以不断学习调节转子实际位置与期望位置的差值,对电机转动速度和交轴给定电流进行在线补偿,使侧杆位置不断向期望位置逼近。通过仿真实验对基于ILC算法的侧杆位置伺服系统进行了验证,结果表明,所提出的算法相对于传统的PID控制算法,可以有效地提升系统稳态跟踪精度,并具有较好的动态响应性能。

Iterative Learning Control With Incomplete Information： A Survey

Abstract--This paper conducts a survey on iterative learn- ing control （ILC） with incomplete information and associated control system design, which is a frontier of the ILC field. The incomplete information, including passive and active types, can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection, storage, transmission, and processing, such as data dropouts, delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects： the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory. Index Terms--Data dropout, data robustness, incomplete in- formation, iterative learning controi（ILC）, quantized control, sampled control, varying lengths.

Iterative Learning Control for a Class of Linear Discrete-time Switched Systems

在这份报纸，反复的学习控制(ILC ) 与任意的切换的信号为线性分离时间的交换系统的一个类被考虑。交换系统重复地在有限时间间隔期间被操作，这被假定，然后第一个顺序 P 类型 ILC 计划能被用来完成完美的追踪在上自始至终间隔。由超级向量途径，为在重复领域的如此的 ILC 系统的一个集中条件能被给。理论分析被模拟支持。

Iterative Learning Control for Discrete-time Stochastic Systems with Quantized Information

An iterative learning control (ILC) algorithm using quantized error information is given in this paper for both linear and nonlinear discrete-time systems with stochastic noises. A logarithmic quantizer is used to guarantee an adaptive improvement in tracking performance. A decreasing learning gain is introduced into the algorithm to suppress the effects of stochastic noises and quantization errors. The input sequence is proved to converge strictly to the optimal input under the given index. Illustrative simulations are given to verify the theoretical analysis. © 2014 Chinese Association of Automation.

Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties

This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.

Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems

In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.

基于改进ILC的蛋白酶发酵过程pH值控制方法

针对海洋蛋白酶(MP)发酵过程中,产酶菌株YS-80酸碱度难以稳定控制在最优范围内的问题,提出一种基于改进迭代学习控制(ILC)的pH值控制方法。分析MP发酵过程动力学模型,确定控制变量;对BP神经网络进行训练和测试,得出迭代学习控制器的增益参数与控制误差之间的映射关系,采用优化后的人工鱼群算法(AFSA)实现增益参数的动态跟踪;建立MP发酵过程pH值迭代学习控制模型,解决YS-80产酸曲线和生长曲线未知的难题。仿真结果表明:该控制方法对基质浓度、菌体浓度的跟踪误差小于0.669 g/L,对相对酶活的跟踪误差小于0.98%,鲁棒性与响应速度良好。

Stochastic Iterative Learning Control With Faded Signals

Stochastic iterative learning control(ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists.Illustrative examples are provided to verify the effectiveness of the proposed schemes.