不确定线性系统迭代学习控制器的设计

推导出采用闭环动态迭代学习律的不确定线性系统收敛充分条件, 把它转化为系统输出反馈H_∞控制问题, 然后用线性矩阵不等式 (LMI)方法系统设计迭代学习控制器, 控制器具有鲁棒性较强、阶次较低等优点. 最后仿真结果表明该设计方法的有效性.     

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。     

基于Vinnicombe距离的迭代辨识与控制设计

迭代辨识与控制设计的主要问题在于保证控制设计的稳定性和闭环性能的不断改善. 针对该问题, 本文提出一种基于Vinnicombe距离的迭代辨识与控制设计方法. 每次迭代辨识使用上次设计的控制器, 通过闭环辨识得到包含真实系统的不确定模型集合, 设计镇定这个集合的控制器; 同时提出迭代辨识过程闭环性能改善条件, 以及控制器的设计方法. 仿真结果显示了该方法的有效性.     

Stability analysis and synthesis for an affine fuzzy control systemvia LMI and ILMI: a continuous case

A new stability analysis and controller synthesis methodology for a continuous affine fuzzy system is proposed in this paper. The method suggested is based on the numerical convex optimization techniques. In analysis, the stability condition under which the affine fuzzy system is quadratically stable is derived and is recast in the formulation of linear matrix inequalities (LMIs). The emphasis of this paper, however, is on the synthesis of fuzzy controller based on the derived stability condition. In the synthesis, the stabilizability condition turns out to be in the formulation of bilinear matrix inequalities and is solved numerically in an iterative manner. Fuzzy local controllers also assume the affine form and their bias terms are solved in a numerical manner simultaneously together with the gains. Continuous iterative LMI (ILMI) approach is presented to obtain a feasible solution for the synthesis of the affine fuzzy system     

Stability analysis and synthesis for an affine fuzzy system via LMIand ILMI: discrete case

This paper develops a stability analysis and controller synthesis methodology for a discrete affine fuzzy system based on the convex optimization techniques. In analysis, the stability condition under which the affine fuzzy system is quadratically stable is derived. Then, the condition Is recast in the formulation of Linear Matrix Inequalities (LMI) and numerically addressed. The emphasis of this paper, however, is on the synthesis of fuzzy controller based on the derived stability condition. In synthesis, the stabilizability condition turns out to be in the formulation of nonconvex matrix inequalities and is solved numerically in an iterative manner. Discrete iterative LMI (ILMI) approach is proposed to obtain the feasible solution for the synthesis of the affine fuzzy system. Finally, the applicability of the suggested methodology is demonstrated via some examples and computer simulations.     

设备状态的多项式神经网络迭代多步预测法

设备状态趋势预测技术是实现设备预知维护的重要手段。为了实现设备的预知维护,在BP算法和一般多步预测法的基础上,提出设备状态的多项式神经网络迭代多步预测法。方法具有较强泛化能力和较快收敛速度的多项式神经网络,采用迭代法更新样本中的历史数据,逐次训练预测模型进行时间序列预测。将方法应用于某石化企业压缩机振动峰峰值的时间序列预测上,并与一般多步预测法相比,多项式神经网络迭代多步预测法在短期预测中改进效果不大明显,但在中长期预测中则具有更高的预测精度。测试结果证明,多项式神经网络迭代多步预测法能更好地满足工程实际应用需要,为设备预知维护提供了一个新思路。     

Adaptive iterative learning control for robot manipulators

In this paper, we propose some adaptive iterative learning control (ILC) schemes for trajectory tracking of rigid robot manipulators, with unknown parameters, performing repetitive tasks. The proposed control schemes are based upon the use of a proportional-derivative (PD) feedback structure, for which an iterative term is added to cope with the unknown parameters and disturbances. The control design is very simple in the sense that the only requirement on the PD and learning gains is the positive definiteness condition and the bounds of the robot parameters are not needed. In contrast to classical ILC schemes where the number of iterative variables is generally equal to the number of control inputs, the second controller proposed in this paper uses just two iterative variables, which is an interesting fact from a practical point of view since it contributes considerably to memory space saving in real-time implementations. We also show that it is possible to use a single iterative variable in the control scheme if some bounds of the system parameters are known. Furthermore, the resetting condition is relaxed to a certain extent for a certain class of reference trajectories. Finally, simulation results are provided to illustrate the effectiveness of the proposed controllers.     

基于迭代式动态规划的影像密集匹配

针对动态规划影像密集匹配中因匹配的整体相关性导致的误匹配点连带扩散效应问题,提出了一种基于迭代式动态规划的影像密集匹配算法。该算法在影像密集匹配过程中引入了视差方向一致性、视差突变性作为迭代判定准则,通过对动态规划匹配结果中不满足迭代条件的候选匹配点子集进行分析,在候选匹配点子集中识别出误匹配点并去除之,反复迭代直至满足迭代准则,从而解决了误匹配点的连带扩散效应问题。该算法已成功应用于嫦娥三号遥操作项目,经在轨应用的检验表明,提出的基于迭代式动态规划的影像密集匹配算法能够极大地降低立体影像匹配中密集同名点的误匹配率。     

Gradient-based maximal convergence rate iterative method for solving linear matrix equations

This paper is concerned with numerical solutions to general linear matrix equations including the well-known Lyapunov matrix equation and Sylvester matrix equation as special cases. Gradient based iterative algorithm is proposed to approximate the exact solution. A necessary and sufficient condition guaranteeing the convergence of the algorithm is presented. A sufficient condition that is easy to compute is also given. The optimal convergence factor such that the convergence rate of the algorithm is maximized is established. The proposed approach not only gives a complete understanding on gradient based iterative algorithm for solving linear matrix equations, but can also be served as a bridge between linear system theory and numerical computing. Numerical example shows the effectiveness of the proposed approach.     

Gradient based iterative solutions for general linear matrix equations

In this paper, we present a gradient based iterative algorithm for solving general linear matrix equations by extending the Jacobi iteration and by applying the hierarchical identification principle. Convergence analysis indicates that the iterative solutions always converge fast to the exact solutions for any initial values and small condition numbers of the associated matrices. Two numerical examples are provided to show that the proposed algorithm is effective.     

On initial conditions in iterative learning control

Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of iterative learning control methods. In this note, we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding learning convergence (or boundedness) property. The iterative learning control method under consideration is based on Lyapunov theory, which is suitable for plants with time-varying parametric uncertainties and local Lipschitz nonlinearities.     

基于消息传递的LDPC码硬判决解码算法建模

提出了一种以奇偶校验和作为消息传递的LDPC码硬判决的解码方案．该方案以奇偶校验方程是否满足约束为条件，从而决定接收分组中的错误位，并对错误位进行翻转．分析了迭代消息流传递机制和迭代解码过程，最后提出一种具体可实现的解码算法模型。     

基于折扣广义值迭代的智能最优跟踪及应用验证

设计了一种基于折扣广义值迭代的智能算法,用于解决一类复杂非线性系统的最优跟踪控制问题.通过选取合适的初始值,值迭代过程中的代价函数将以单调递减的形式收敛到最优代价函数.基于单调递减的值迭代算法,在不同折扣因子的作用下,讨论了迭代跟踪控制律的可容许性和误差系统的渐近稳定性.为了促进算法的实现,建立一个数据驱动的模型网络用...     

Iterative Consensus for a Class of Second-order Multi-agent Systems

In this paper, the problem of leader-following consensus for a class of multi-agent systems with double integrator dynamics is investigated based on an iterative learning approach. Consensus errors of individual agents are considered as the anticipation in time, based on which a distributed iterative learning protocol is proposed for the undirected networks with fixed topology to make the followers track the leader in finite time. The dynamic of the leader is assumed to be time-varying and the state information is available to only a portion of the followers. The sufficient condition for solving the consensus problem of the multi-agent system is obtained. A simulation example is provided to demonstrate the effectiveness of the proposed method.     

A method for optimal limit design of structures with alternative loads

Three problems of structural optimization are formulated and solved either by a direct or an iterative method. The first problem, the most general considered here, concerns “cost-optimization” of perfectly plastic structures for alternative loads. The second and third ones are more specific problems with linear objective functions, quadratic yield conditions and either a single or several load conditions. The linear-quadratic case can be solved directly when only one load condition is considered. This problem is shown to be related to elastic analysis. For alternative loads an iterative method based on this analysis is proposed. It shows strong convergence and forms the basis of an iterative solution of the general problem.     

无线传感器网络中一种基于多维定标的迭代定位算法

针对基于经典多维定标的MDS-MAP算法在定位精度与矩阵计算复杂度方面的不足,提出一种基于多维定标的迭代定位算法.该算法仅使用节点间的一跳距离,通过新的多维定标的迭代算法进行定位.该算法复杂度不高,理论分析和仿真结果表明,在规则网络中定位误差较小,最优情况下误差可以小于5%,在非规则网络中仍可达到较好的定位精度.     

The convergence conditions of the psd iterative method for linear systems

In this paper, the PSD iterative method was proposed by Evans and Missirlis [4], for solving a large nonsingular system of linear equations Ax=b A general necessary condition for con-vergence of the PSD iterative method is obtained. The convergence theorems of the PSD iterative method are established under the condition that the coefficient matrix A is an H-matrix, our theorems improve and extend some known results.     

一阶强双曲分布参数系统的迭代学习控制

研究了一阶强双曲分布参数系统的迭代学习控制问题.首先利用Fourier变换和半群方法导出了系统状态的适应解.进而基于强双曲条件和Plancheral定理,在允许迭代过程中初值存在一定偏差条件下,给出并证明了系统在P型迭代学习控制算法下的收敛条件.最后应用实例说明了所提方法的有效性.     

应用改进的弹簧质点模型进行图像滤波的算法

为了克服单一使用中值滤波方法去除脉冲噪声会造成图像细节信息丢失的缺陷,提出一种基于弹簧质点模型检测的迭代中值滤波算法.首先将被检测点作为中心点,其周围8个方向的像素点对该中心点的拉力组成一个平面内的弹簧质点模型,根据弹簧质点模型的稳定条件,即平面汇交力系的平衡原理来检测像素点是否为噪声点;然后通过迭代方法,只用信号点来修改噪声点的像素值.实验结果表明,与传统的滤波算法相比,文中算法可以更有效地去除图像中的脉冲噪声并且保留原图像的细节.     

基于属性重要性的决策表属性约简算法

提出一种基于粗糙集属性重要性的属性约简算法。该算法以所有条件属性为初始约简集合,以属性重要性为迭代准则,通过逐步缩减来求取约简。同时给出了该算法的时间复杂度分析,并举例验证了所提出算法的有效性和实用性。