不确定离散线性系统的鲁棒单调反馈–前馈迭代学习控制

针对一类不确定离散线性系统,提出一种沿迭代方向鲁棒单调收敛和沿时间方向有界输入有界输出(bouned-input bounded-output,BIBO)稳定的反馈–前馈迭代学习控制策略.首先,将不确定反馈–前馈迭代学习系统表示为不确定二维Roesser模型系统;然后,把二维系统沿迭代方向的鲁棒单调收敛问题转化成一维系统的H∞干扰抑制控制问题,并给出系统的稳定性证明和用线性矩阵不等式(linear matrix inequality,LMI)表示的沿迭代方向鲁棒单调收敛的充分条件,该LMI充分条件不仅可以用于确定反馈–前馈控制器的增益矩阵,而且还可以保证系统沿时间轴方向是BIBO稳定的;最后,仿真结果证明了该反馈–前馈迭代学习控制策略的有效性.     

变时滞T-S模糊系统的L∞静态输出反馈控制

针对一类具有状态变时滞的 T-S 模糊非线性系统, 通过定义一类时滞相关 Lyapunov 函数, 引入基于 L∞ 范数理论的鲁棒性能指标, 并利用线性矩阵不等式(LMI), 给出了模糊鲁棒输出反馈控制器的设计方法和存在的充分条件, 保证整个闭环系统的渐近稳定性. 最后, 倒车控制系统的仿真表明了本方法的有效性.     

执行器故障时变系统的PID迭代容错控制

对存在执行器故障的连续线性时变系统，给出了PID型迭代学习容错控制律的收敛条件。对连续时变故障系统设计了一种PID迭代学习容错控制律，在[λ]范数意义下给出了故障系统PID型迭代容错控制器收敛的充要条件；基于Schur补原理和不等式变换，将容错控制器收敛条件转换成线性矩阵不等式，当迭代学习收敛速度设定时，基于线性矩阵不等式能快速确定最优迭代控制增益，避免了迭代控制增益设置的盲目性。旋转控制系统的数值仿真，验证了PID迭代容错控制器优良的容错性能和跟踪性能。     

不确定线性2-D 离散系统的鲁棒滑模控制

针对不确定线性离散二维（2-D）系统,研究了其鲁棒稳定性、鲁棒镇定和鲁棒滑模控制问题.基于线性矩阵不等式的方法推导了该系统鲁棒渐近稳定的充分条件,并给出了系统状态反馈镇定器和理想滑动模态存在的充分条件.改进了离散时间滑模控制系统的趋近律方法,使得状态能够到达滑模面上产生理想的滑动模态,并将其推广应用到2-D离散系统中,综合了一类滑模控制器保证闭环系统鲁棒渐近稳定.仿真实例证实了该设计方法的有效性.     

T-S 模糊时滞模型的时滞相关稳定性分析和镇定

研究一类用T—S模糊模型描述的非线性不确定时滞系统的时滞相关鲁棒镇定问题．基于线性矩阵不等式的可行解，首先给出利用T—S模糊模型描述的非线性时滞系统时滞相关稳定性准则；然后给出了经状态反馈鲁棒镇定设计的新方法．所设计的控制器能确保闭环系统渐近稳定．     

一类带有时滞的不确定广义系统的切换渐近稳定性

研究了一类带有时滞的切换不确定广义系统的鲁棒渐近稳定问题. 利用Lyapunov稳定性定理和线性矩阵不等式(Linear matrix inequality, LMI)工具, 采用多Lyapunov函数技术, 在设定的切换律下, 得到切换不确定广义时滞系统鲁棒渐近稳定的时滞相关充分条件. 进一步, 建立了一个具有线性矩阵不等式约束的凸优化问题, 利用Matlab软件中的LMI工具箱求解, 得到保证切换广义系统鲁棒渐近稳定的最大可允许时滞上界. 最后示例表明了该方法的有效性.     

不确定广义时滞系统的鲁棒稳定方法研究

研究一类带有时滞不确定广义系统的鲁棒渐近稳定问题．利用Lyapunov稳定性定理和线性矩阵不等式工具，得到了广义系统正则、鲁棒渐近稳定的时滞相关充分条件．为了降低所得结果的保守性，避免了采用系统模型变换和利用矩阵不等式方法．进一步，建立一个具有线性矩阵不等式约束的凸优化问题，利用Matlab软件中的LMI工具箱求解，得到保证广义系统鲁棒渐近稳定的最大可允许时滞上界．仿真示例表明了该方法的有效性．     

基于模糊T-S模型的非线性系统的H∞鲁棒容错控制

研究一类具有不确定和时滞的非线性系统的H鲁棒容错控制问题.采用T-S模糊模型来描述非线性系统,在系统执行器失效的情况下,建立故障矩阵模型;通过引进自由加权矩阵,基于Lyapunov稳定性理论和LMI(线性矩阵不等式)方法,给出系统H鲁棒容错控制器存在的充分条件,保证了系统的鲁棒稳定性,仿真实例验证了该方法的有效性.     

非线性不确定中立型系统的鲁棒H∞控制

考虑系统外界干扰、系统参数摄动等非线性扰动环节对中立型时滞系统的H∞影响,提出基于Lyapunov稳定性理论的鲁棒H∞控制器的设计思想.利用线性矩阵不等式(LMI)方法,给出了该类具有状态非线性不确定性中立型时滞系统的鲁棒∞控制器的设计实例.在非线性不确定函数满足增益有界的条件下,得到了该类时滞系统满足鲁棒∞性能的一个充分条件.通过求解一个线性矩阵不等式LMI,即可获得鲁棒∞控制器.仿真结果表明了基于Lyapunov稳定性理论,LMI技术设计的控制器克服了系统外界非线性干扰或系统本身非线性参数摄动的影响,实现了闭环系统的H∞性能条件下的渐近稳定,满足了该系统鲁棒H∞控制的要求.     

不确定时滞系统的鲁棒绝对稳定性研究

基于Lyapunov稳定性理论，采用线性矩阵不等式处理方法，研究具有参数不确定性的时滞系统鲁棒绝对稳定性问题．导出了用线性矩阵不等式系统可行性描述的系统鲁棒绝对稳定的滞后依赖型条件，据此可计算出最大的允许时滞界．通过求解一组线性矩阵不等式，给出使得闭环系统鲁棒绝对稳定的无记忆状态反馈控制律设计方法。     

Feedback iterative learning control for time-delay systems based on 2D analysis approach

This paper deals with the iterative learning control (ILC) design for multiple-input multiple-output (MIMO), time-delay systems (TDS). Two feedback ILC schemes are considered using the so-called two-dimensional (2D) analysis approach. It shows that continuous-discrete 2D Roesser systems can be developed to describe the entire learning dynamics of both ILC schemes, based on which necessary and sufficient conditions for their stability can be provided. A numerical example is included to validate the theoretical analysis.     

Quantized Iterative Learning Control Design For Linear Systems Based On A 2‐D Roesser Model

This paper considers the problem of iterative learning control design for linear systems with data quantization. It is assumed that the control input update signals are quantized before they are transmitted to the iterative learning controller. A logarithmic quantizer is used to decode the signal with a number of quantization levels. Then, a 2‐D Roesser model is established to describe the entire dynamics of the iterative learning control (ILC) system. By using the sector bound method, a sufficient asymptotic stability condition for such a 2‐D system is established and then the ILC design is given simultaneously. The result is also extended to more general cases where the system matrices contain uncertain parameters. The effectiveness of the proposed method is illustrated by a numerical example.     

基于2_D系统理论的变初始条件线性系统的开闭环学习控制

对于具有重复运动特性的系统,迭代学习控制是一种简便且行之有效的方法.考虑到系统每次运行初始值的不确定性,利用二维系统理论设计了开闭环迭代学习控制器,并给出了控制收敛的充分条件.用仿真试验验证其有效性.     

第二FM模型的两维离散状态时滞系统H∞控制

本文研究了基于第二 FM 模型的两维离散状态滞后系统 H∞ 控制问题. 给出了这类两维系统具有一定 H∞ 扰动抑制度的一个线性矩阵不等式条件, 并通过求解一个凸优化问题得到一个最优 H∞ 控制器. 最后通过一个仿真例子验证所提结果的有效性.     

迭代学习控制综述

系统地论述了迭代学习控制的发展和研究现状,包括学习算法及其各种分析方法、与其他控制技术的结合及其应用都作了的总结.重点对迭代学习控制研究的前沿问题:基于频域分析的迭代学习控制、基于2-D理论的迭代学习控制、基于Lyapunov直接法的迭代学习控制、最优化迭代学习控制和采样迭代学习控制进行阐述.最后讨论了目前研究中存在的问题及未来的研究方向.     

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.     

Robust design of iterative learning control for a batch process described by 2D Roesser system with packet dropouts and time‐varying delays

Batch process, working as a best choice for low‐volume and high‐value products in manufacturing, has been widely used in chemical industries. The actuator faults and time delays often occur in practical production. This paper develops an iterative learning control (ILC) design for a batch process described by two‐dimensional (2D) Roesser system with packet dropouts and time‐varying delays. The phenomenon of actuator faults is regarded as an arbitrary stochastic sequence satisfying the Bernoulli random binary distribution. Firstly, the ILC design for a batch process is transformed into stability analysis for a 2D stochastic system with time‐varying delays. Secondly, for analyzing the stability of 2D stochastic systems, we derive the stability condition in terms of linear matrix inequality. Then, we give a procedure to get the control gain for the ILC design. An injection modeling process as an example with simulations in different cases of data dropout is given to demonstrate the validity of the proposed method. Furthermore, the proposed method has a better result by comparing the existing methods.     

二维随机FM-II系统的状态估计

This paper is concerned with state estimation of two-dimensional (2-D) discrete stochastic systems. First, 2-D discrete stochastic system model is established by extending system matrices of the well-known Fornasini-Marchesini's second model into stochastic matrices. Each element of these stochastic matrices is second-order weakly stationary white noise sequences. Secondly, a linear and unbiased full-order state estimation problem for 2-D discrete linear stochastic model is formulated. Two estimation problems considered are the designs for the mean-square bounded estimation error and for the mean-square stochastic version of the suboptimal H∞ estimator, respectively. Our results can be seen as extensions of the 2-D linear deterministic case. Finally, illustrative examples are provided.     

受扰动2-D线性时变系统的迭代学习控制

利用2-D系统理论的Roesser模型,给出了受扰动的线性时变离散系统迭代学习控制(ILC)问题的一种解决方法.对系统所受的已知扰动,给出其学习律参数的选取范围以及仅经一次迭代就能实现输出完全跟踪期望轨迹的参数选取方法;对系统所受的未知扰动,首先对SISO系统提出其学习律存在的条件及参数选取方法,进而推广到MIMO系统中,提出MIMO系统学习律的参数选取方法.最后给出两个数值例子进一步说明所得结果的有效性.     

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.