基于迭代学习的离散切换系统故障估计

针对一类具有任意切换序列的离散切换系统的故障估计问题,提出了一种新的故障估计算法.该算法利用引入的虚拟故障信号构建出故障估计器,并利用残差信号通过迭代学习方法对引入的虚拟故障进行逐次修正.使虚拟故障随着迭代次数的增加逐渐逼近实际故障.利用压缩映射方法严格证明了算法在各个子区间上的收敛性,给出了算法的收敛条件.理论分析表明,所提算法能够在有限区间上精确估计出切换系统发生的不同类型故障.最后通过仿真实验进一步验证了所提算法的有效性.     

离散时间切换广义系统的一致有限时间稳定性

针对一类离散时间切换广义系统, 研究其一致有限时间稳定性问题. 首先, 把广义系统的有限时间稳定性概念推广到离散切换广义系统; 然后, 利用Lyapunov-like函数方法, 给出了离散切换广义系统在任意给定的切换规则下是正则、 因果的, 且有限时间有界和有限时间稳定的充分条件, 同时给出了保证离散切换广义系统一致有限时间稳定的状态反馈控制器的具体设计方法. 仿真算例结果说明了该控制方法的有效性.     

时变离散切换系统的迭代学习控制

针对一类具有任意切换序列的时变离散切换系统的轨迹跟踪问题,提出了一种离散迭代学习控制算法.该算法在切换序列沿迭代轴不变而只沿时间轴变化的前提下,将整个有限时间区间划分为有限个子区间,并利用λ-范数对各个子区间上的收敛性进行了严格证明,给出了算法收敛的范数形式的充分条件.该方法不仅实现了时变离散切换系统在有限时间内对目标轨迹的完全跟踪,而且结构简单更便于工程实现.仿真结果验证了该方法的有效性.     

基于相息图迭代的随机相位加密

提出了一种将随机相位加密和相位恢复算法中的求解附加相位分布分二步实施的加密方法. 由于该方法的实质是通过在随机谱和相息图之间进行相位恢复迭代以确定相息图和密钥的相 位分布，因而能够减小图像的解密误差.在相息图相位离散化的迭代过程中，采用增大设计 冗余度的方法，降低了由相位离散化所带来的解密误差.最后，通过计算机模拟实验验证了 该方法在减小图像解密误差方面的有效性. 关键词： 随机相位 光学图像加密 相息图 二元光学 离散化误差 相位恢复算法     

应用于声反馈抑制的变步长归一化子带自适应滤波算法*

针对自适应滤波算法中稳态失调量和收敛速度之间的矛盾,提出了一种新的变步长归一化子带自适应滤波算法。该算法在系统噪声抵消原理的基础上,用迭代收缩的方法估计得到无噪先验子带误差的功率,对每个子带步长进行更新。对所提出的算法进行数学分析,可以得出该算法是稳定的和收敛的。在长回声路径和短回声路径两种情况下,将该算法应用于助听器声反馈抑制系统中。相对于其他归一化子带自适应滤波算法,仿真实验表明,所提算法实现了更快的收敛速度,获得了更低的失调量。     

基于DCT级联ICF算法降低相干光正交频分复用系统峰均抑制比（英文）

为解决相干光正交频分复用中高峰均抑制比的问题,提出一种离散余弦变换(DCT)和迭代限幅滤波(ICF)级联的新算法。ICF算法是对限幅算法进行改善,添加了迭代过程,可以改善限幅运算对系统误码率的影响。基于DCT算法有较小的误码率和低计算复杂度的特点,所提算法对频域调制信号进行离散余弦变换,以降低高峰值信号的概率,再通过级联ICF算法来实现对峰均抑制比的降低。结果表明,与原始信号相比,当互补累积分布函数为10^-4时,所提算法的优化幅度为4.685 dB。在误码率为10^-3时,所提算法的光信噪比为20.96 dB,同时能做到长距离传输。除此之外,所提方案的总计算量为19 970。仿真数据表明,考虑峰均抑制比、误码率和计算复杂度等综合因素,所提方案性能最优。     

无线携能通信系统中基于能量获取比例公平的波束成形设计

针对无线携能通信系统中存在能量获取不均衡的问题, 提出了一种基于能量获取比例公平的波束成形设计方案. 该方案在满足信息接收者的信干噪比以及发送端的最大发送功率等约束条件的基础上, 通过优化波束矢量实现能量获取的比例公平. 此设计在数学上是一个很难直接求解的非凸优化问题.为此, 本文首先利用半定松弛技术将其转换为半定规划问题, 然后结合二分法提出了可以获得最优波束矢量的迭代算法.此外, 在发送端仅知道部分信道状态信息且知道信道误差范围的情况下, 采用最差性能最优的方法对原优化问题进行了鲁棒波束成形设计, 并提出了相应的迭代算法. 仿真结果表明所提算法均可实现能量获取的比例公平且性能达到全局最优.     

自治混沌系统的线性和非线性广义同步

研究了自治混沌系统的广义同步问题.基于改进的状态观测器方法和极点配置技术,提出了一种新的广义同步方案,扩展了混沌广义同步的适用范围,并用该方法实现了自治混沌系统的线性及非线性广义同步.根据状态观测器理论,给出了驱动-响应系统获得全局渐进广义同步的充分条件.数值仿真实验进一步验证了所提方法的有效性. 关键词： 自治混沌系统 广义同步 状态观测器 分段线性Chen系统     

基于广义正交迭代算法的立体视觉定位

提出了一种新的基于广义正交迭代算法的立体视觉定位.该算法通过提取CenSurE局部特征和相应的U-SURF描述符,采用SAD方法进行子像素立体匹配,并利用U-SURF描述符匹配进行前后帧图像特征跟踪.在RANSAC框架下对匹配点进行3D-3D运动估计获得了运动参量的初始值.由于3D-3D运动估计使3D点集间欧式距离误差最小,而3D特征点坐标受噪音影响很大,因此运动估计误差也较大.本文把广义正交迭代算法应用到立体视觉定位方法中,得到使立体相机目标空间共线性误差最小的运动估计参量,由于共线性误差比3D-3D运动估计算法中的共点误差受噪音影响更小,从而大大较少了运动估计误差.仿真实验和户外真实实验表明：本文算法获得了较高的计算准确度、鲁棒性和实时性,优于传统方法.     

自治混沌系统的线性和非线性广义同步

研究了自治混沌系统的广义同步问题.基于改进的状态观测器方法和极点配置技术，提出了一种新的广义同步方案，扩展了混沌广义同步的适用范围，并用该方法实现了自治混沌系统的线性及非线性广义同步.根据状态观测器理论，给出了驱动-响应系统获得全局渐进广义同步的充分条件.数值仿真实验进一步验证了所提方法的有效性.     

Approximation-error-ADP-based optimal tracking control for chaotic systems with convergence proof

In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming （ADP） algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.     

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

A policy iteration algorithm of adaptive dynamic programming(ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then,the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks,the developed optimal tracking control scheme for chaotic systems is verified by a simulation.     

Enhanced iterative learning control for a piezoelectric actuator system using wavelet transform filtering

For trajectory tracking of a piezoelectric actuator system, an enhanced iterative learning control (ILC) scheme based on wavelet transform filtering (WTF) is proposed in this research. The enhanced ILC scheme incorporates a state compensation in the ILC formula. Combining state compensation with iterative learning, the scheme enhances tracking accuracies substantially, in comparison to the conventional D-type ILC and a proportional control-aided D-type ILC. The wavelet transform is adopted to filter learnable tracking errors without phase shift. Based on both a time-frequency analysis of tracking errors and a convergence bandwidth analysis of ILC, a two-level WTF is chosen for ILC in this study. The enhanced ILC scheme using WTF was applied to track two desired trajectories, one with a single frequency and the other with multiple frequencies, respectively. Experimental results validate the efficacy of the enhanced ILC in terms of the speed of convergence and the level of long-term tracking errors.     

轮式机器人轨迹跟踪控制系统的设计

针对轮式机器人轨迹跟踪控制系统误差收敛速率低、精度和实时性差的问题,采用反演控制算法并结合李雅普诺夫稳定性分析方法对轮式机器人的轨迹跟踪系统进行了优化设计。建立了轮式机器人轨迹跟踪控制系统的运动学模型,并对该模型进行位置偏差分析;在反演控制算法中引入了分部虚拟控制量,并分析和设计了其他间接受控量,提高了算法运行的效率;采用李雅普诺夫收敛定理对系统的收敛性进行分析,根据分析的结果提出了算法更加简单的控制律。利用Matlab软件的Simulink库对设计的轨迹跟踪控制系统试验研究。结果表明,与基于李雅普诺夫直接法或者迭代学习算法设计的轮式机器人轨迹跟踪控制系统相比较,设计的控制系统具有跟踪精度高、收敛速度快、实时性好的优点。     

无线光码分多址中一种改进的盲多用户检测算法

干扰消除是无线光码分多址(CDMA)通信系统中的一项关键技术。分析了基于递归最小二乘(RLS)算法的盲多用户检测算法的原理及性能,并将其应用于采用光正交码的无线光CDMA系统中。讨论了该系统中遗忘因子的选取原则及其对算法的收敛性和系统误码率性能的影响。为了克服固定遗忘因子所带来的矛盾,提出了一种改进的变遗忘因子RLS盲多用户检测算法,并对遗忘因子进行了修正。结果表明,采用改进的方法后系统能获得较快的收敛速度和跟踪速度,收敛时的估计误差也较小,而且信号干扰比由传统算法的6dB提高到9dB。说明采用变遗忘因子的RLS算法不仅适合于采用光正交码的无线光CDMA系统,而且跟踪期望用户信号的性能良好。     

An adaptive inverse iteration for Maxwell eigenvalue problem based on edge elements

We propose and analyze an adaptive inverse iterative method for solving the Maxwell eigenvalue problem with discontinuous physical parameters in three dimensions. The adaptive method updates the eigenvalue and eigenfunction based on an a posteriori error estimate of the edge element discretization. At each iteration, the involved saddle-point Maxwell system is transformed into an equivalent system consisting of a singular Maxwell equation and two Poisson equations, for both of which preconditioned iterative solvers are available with optimal convergence rate in terms of the total degrees of freedom. Numerical results are presented, which confirms the quasi-optimal convergence of the adaptive edge element method in terms of the numerical accuracy and the total degrees of freedom.     

Optimal tracking control with zero steady-state error for time-delay systems with sinusoidal disturbances

The problem of optimal tracking control with zero steady-state error for linear time-delay systems with sinusoidal disturbances is considered. Based on the internal model principle, a disturbance compensator is constructed such that the system with external sinusoidal disturbances is transformed into an augmented system without disturbances. By introducing a sensitivity parameter and expanding power series around it, the optimal tracking control problem can be simplified into the problem of solving an infinite sum of linear optimal control series without time-delay and disturbance. The obtained optimal tracking control law with zero steady-state error consists of accurate linear state feedback terms and a time-delay compensating term, which is an infinite sum of an adjoint vector series. The accurate linear terms can be obtained by solving a Riccati matrix equation and a Sylvester equation, respectively. The compensation term can be approximately obtained through a recursive algorithm. A numerical simulation shows that the algorithm is effective and easily implemented, and the designed tracking controller is robust with respect to the sinusoidal disturbances.     

Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach

A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems.