Semiglobal stabilization of saturated linear systems via multiple parametric Lyapunov equations

This paper investigates the problem of semiglobal stabilization with guaranteed flexible pole placement for saturated linear systems. To retain the advantages of the parametric Lyapunov equation, matrix‐partitioning idea is used to derive a new pole shift lemma. Starting from system matrix transformations, a recursive algorithm is proposed to shift every eigenvalue of a linear system separately without mode decomposition in each step. A new method introducing various parameters to every Lyapunov equation in each step is presented. As an application, the semiglobal stabilization with guaranteed flexible pole placement for saturated linear systems can be achieved by this method. Finally, its effectiveness and advantages are demonstrated via a simulation example. Copyright © 2013 John Wiley & Sons, Ltd.     

On backward shift algorithm for estimating poles of systems

In this paper, we present an algorithm for estimating poles of linear time-invariant systems by using the backward shift operator. We prove that poles of rational functions, including zeros and multiplicities, are solutions to an algebraic equation which can be obtained by taking backward shift operator to the shifted Cauchy kernels in the unit disc case. The algorithm is accordingly developed for frequency-domain identification. We also prove the robustness of this algorithm. Some illustrative examples are presented to show the efficiency in systems with distinguished and multiple poles.     

Iterative SNR estimation using a priori information

A class iterative signal-to-noise ratio (SNR) estimation algorithm is proposed in this paper. The data samples are governed by a given distribution with a priori. The expectation maximization (EM) algorithm is applied to iteratively maximize the likelihood function so as to realize the SNR estimation. Cramer–Rao bounds (CRB) with different a priori are compared for binary phase shift keying and orthogonal phase shift keying systems, which show the potential of the SNR estimator in turbo-like systems. In high-order modulations, simulation results show that the reduced-complexity iterative method with equal a priori has better performance in middle or high SNR region than the foregone ones. Moreover, the new method with feedback information is the best when its iteration number is 4 and extrinsic information larger than 0.4. These methods are applied in the bit-interleaved coded modulation with iterative decode (BICM-ID) system to validate the effect of the proposed methods.     

Simultaneous estimation of the input and output frequencies of nonlinear systems

In this paper, the simultaneous estimation of the input and output frequencies of nonlinear systems is considered. As the output frequencies are generated from the input frequencies, and are integer combinations of these frequencies, it is shown in this paper that the simultaneous estimation of both the input and output frequencies can therefore be formulated as a constrained estimation problem. First, the constrained Cramér-Rao lower bound, an important general property of any unbiased estimator, is derived. The procedure and algorithm for estimating the input and output frequencies are devised based on the periodogram method. Numerical examples are presented to illustrate the performance and implementation of the proposed estimation procedure and algorithm.     

线性离散周期系统满意估计

针对线性离散周期系统的状态估计问题,运用提升原理提取期望极点指标,同时期望估计误差系统满足稳态误差方差/H_∞混合指标,采用代数Riccati矩阵不等式法与数值递推算法对误差系统进行了上述指标的满意估计设计,并根据满意控制的基本理论将上述满意估计问题转化为线性矩阵不等式(LMI)的线性规划问题,从而运用LMI技术求解并设计了可行的满意估计,数值算例验证了相关算法.     

Design weighting parameter tuning of multivariable self-tuning controllers

In this paper, we present the method for on-line tuning of a design weighting polynomial parameters of a multivariable self-tuning controller which adapts to changes in the higher-order nonminimum phase system parameters with time delays and noises. The algorithm effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins–Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed method is simple and effective compared with the pole restriction method. The computer simulation results are presented to adapt the higher-order multivariable system with nonminimum phase and with changeable system parameters.     

基于CARIMA模型的多变量极点配置自校正直接算法及其鲁棒性

本文根据 CARIMA 模型,采用双线性一步松驰算法和多项式矩阵展开交换法,给出一种适用于非最小相位系统的多变量极点配置自校正直接算法,并研究了其鲁棒性.此法计算量小,对初值选择不敏感,系统跟踪调节性能好,鲁棒性强,是一种有应用前景的极点配置直接算法。     

Backward Q-learning: The combination of Sarsa algorithm and Q-learning

Reinforcement learning (RL) has been applied to many fields and applications, but there are still some dilemmas between exploration and exploitation strategy for action selection policy. The well-known areas of reinforcement learning are the Q-learning and the Sarsa algorithms, but they possess different characteristics. Generally speaking, the Sarsa algorithm has faster convergence characteristics, while the Q-learning algorithm has a better final performance. However, Sarsa algorithm is easily stuck in the local minimum and Q-learning needs longer time to learn. Most literatures investigated the action selection policy. Instead of studying an action selection strategy, this paper focuses on how to combine Q-learning with the Sarsa algorithm, and presents a new method, called backward Q-learning, which can be implemented in the Sarsa algorithm and Q-learning. The backward Q-learning algorithm directly tunes the Q-values, and then the Q-values will indirectly affect the action selection policy. Therefore, the proposed RL algorithms can enhance learning speed and improve final performance. Finally, three experimental results including cliff walk, mountain car, and cart–pole balancing control system are utilized to verify the feasibility and effectiveness of the proposed scheme. All the simulations illustrate that the backward Q-learning based RL algorithm outperforms the well-known Q-learning and the Sarsa algorithm.     

基于人工蜂群算法的混沌系统参数估计

为了更准确的估计混沌系统的未知参数,提出了一种基于人工蜂群算法的混沌系统参数辨识方法,该方法将混沌系统中参数估计转化为多维变量的函数优化问题,利用搜索方程对多维空间变量进行充分搜索,通过优化人工蜂群算法计算估计值与真值之间的均方差,从而估计出混沌系统的参数. Lorenz混沌系统的参数辨识仿真实验结果表明了该方法的可行性,并且算法收敛速度快,估计精度高.     

Backward errors and small-sample condition estimation for ⋆-Sylveter equations

In this paper, we consider the perturbation analysis for ?-Sylvester equations. Based on the small-sample condition estimation (SCE) technique, we devise algorithms to estimate normwise, mixed and componentwise condition numbers for ?-Sylvester equations. We also define a componentwise backward error with a sharp and easily computable bound. Numerical examples illustrate that our algorithm for componentwise perturbations produces reliable estimates, and the new derived computable bound for the componentwise backward error is sharp and reliable for well-conditioned and moderate ill-conditioned ?-Sylvester equations under large or small perturbations.     

5G低轨卫星移动通信系统多普勒频偏估计算法

随着卫星移动通信系统近些年来的快速发展,高传输速率、广泛覆盖范围、较低的传输时延以及较强灵活性是新一代的5G低轨(LEO)卫星移动通信系统发展的要求,但是由于低轨卫星移动速度很快,产生的较大的多普勒频移。通过建立使用5G候选波形滤波的正交频分复用技术(F-OFDM)的新型低轨卫星移动通信系统,针对该新型系统设计一种新的多普勒频偏估计方法,首先借助卫星轨道的多普勒特性来计算整数多普勒频偏,第二步借助F-OFDM资源块中的导频信息来估计精确的多普勒频移。主要完成了5G低轨卫星移动通信系统模型的建立与算法在系统中的仿真来验证其实际性能,经过仿真可以得到,本算法与同类算法相比计算复杂度低且精度较高。     

A new approach for instantaneous pole placement with recurrent neural networks and its application in control of nonlinear time-varying systems

In this paper, we first show that online computation of feedback gain used for pole placement of nonlinear systems in recent years is not reliable, and then we present a new approach for instantaneous pole placement and apply it with dynamical recurrent neural networks for online computation of feedback gain. Because of high-speed convergence of neural network to feedback gain, we can apply this method for pole placement of nonlinear time-varying systems. One strategy for realization of this method is instantaneous linearization, as we do here by simulation. The advantage of the proposed method is a global uniform asymptotical exponential stability (GUAES) of closed-loop system around the equilibrium point.     

OFDM系统一种新的多普勒频偏估计方法

多普勒频偏估计是移动通信系统中的一个重要研究内容.提出了一种OFDM移动通信系统中新的多普勒频偏估计方法.该方法利用接收信号的功率会随着信道的变化而变化并且变化的速率与最大多普勒频偏有一定的关系这一特点,在接收端测量出每一帧接收信号的平均功率,并把一定数目的平均功率值存储起来,先进行粗估计,再进行细估计,这样就求出了接收信号的功率变化速率,从而查表得到最大多普勒频偏.文中对这一方法进行了仿真验证,仿真结果表明,此方法可以有效的估计多普勒频偏,并且具有实现简单和受噪声影响小的优点.     

基于压缩感知的MIMO-OFDM信道估计中的导频设计方案

多输入多输出-正交频分复用(Multiple input multiple output-orthogonal frequency division multiplexing,MIMO-OFDM)系统作为MIMO系统和OFDM系统的结合,具有很高的频带利用率并能有效地对抗无线信道的多径效应。本文研究了MIMO-OFDM系统稀疏信道估计及其导频优化,将信道估计问题转化为压缩感知(Compressed sensing,CS)理论中的稀疏信号重建问题,将最小化测量矩阵的互相关作为导频优化的目标。结合已有的随机序贯搜索(Stochastic sequential search,SSS)和扩展算法2(Extension scheme 2,ES2)算法以及导频移位机制,提出了一种快速的导频优化算法随机搜索移位算法(Stochastic sequential search-shift mechanism,SSS-SM)。此算法的运算复杂度远低于已有的ES2算法,运算时间不受发射天线数影响。将SSS-SM算法和ES2算法分别获得的导频设计结果应用于MIMO-OFDM系统的信道估计,仿真结果表明,采用SSS-SM算法可以更低的算法复杂度获得与ES2算法相同的信道估计性能;高信噪比情况下,SSS-SM算法对应的均方误差(Mean square error,MSE)比ES2平均低约3~5 dB,因此这种方法在高信噪比下更有优势。     

多输入多输出线性控制系统的简单极点配置算法

本文给出了对多输入多输出系统进行极点配置的算法。按照本文的结论，任何多输入多输出线性控制系统都可以象单输入单输出线性系统那样进行闭环系统及状态观测器的极点配置。     

不确定随机系统的满意估计问题研究

基于LMI技术给出了一个满足期望误差方差和快速性指标的满意状态估计器的设计 方法.该方法避免了使用矩阵秩的条件,因此适用于参数及噪声强度不确定的随机线性系统,而 且便于使用计算机求解.文中所给出的数值算例说明了该方法的有效性.     

一种应用于遥操作系统的机械振动辨识方法

当考虑遥操作系统中轻型机器人的结构振动辨识问题时,由于通讯环节的带宽窄和通讯频率低,传统的振动辨识方法将遭遇瓶颈．因此,研究如何减少振动辨识过程中需要传输的数据量显得尤为重要的．为此,本文借鉴ESPRIT（estimation of signal parameters via rotational invariance techniques）算法,研究了机械结构振动的欠采样辨识问题．提出了ASP（adding sampling points）方法．在ASP中,用平均值矩阵取代了传统ESPRIT算法中的相关矩阵,在保证充分利用采样数据的同时,克服了相关矩阵易产生病态的问题,并减小了计算量;对振动频率和衰减系数分别进行辨识,然后再将它们匹配成对,解决了频率解混叠和衰减系数准确辨识之间的矛盾;利用差异度法匹配振动频率和衰减系数,在匹配过程中可以对辨识出的振动频率进行修正．仿真和实验都证明了该方法不仅可行,而且具有很好的效果．     

具有指定闭环极点的次最优控制系统设计

本文利用多变量系统极点配置的结果,提出了一种具有指定闭环极点,并使二次型指标达到极小的次最优控制系统的设计方法,这种设计方法与标准线性二次型调节器(LQR设计方法的不同之点,就在于设计中引入了闭环极点约束,使闭环系统能具有希望的动态特性,本文还给出了在计算机上易于实现的设计算法和计算过程,以及设计实例。     

具有减少辨识参数的随机多变量极点配置自适应控制器

本文提出了一种新的限制输出个数减少随机多变量自适应控制中辨识参数的方法，并给出了减少辨识参数的极点配置自适应算法。虽然采用ｎ个输入１个输出的减少辨识参数的模型来设计控制器，但所提出的控制器能够保证被控系统的几个输出跟踪参考输入信号，仿真结果表明，所提出的方法是成功的。     

State estimation for multi‐channel stochastic singular systems with multiplicative noise

The state estimation problem for multi‐channel singular systems with multiplicative noise is considered based on singular value decomposition. First, two equivalent reduced order subsystems are obtained via the decomposition. Then, in order to solve the estimation problem, the subsystems are rewritten into a new form. It is noted that the measurement noise here becomes colored noise, which contains the dynamic noise, measurement noise, and multiplicative noise of the original system. In this situation, existing filtering methods cannot be directly applied, so a modified filtering method is given. The recursive algorithm for the state estimation is obtained by the filtering method. In addition, the estimation of dynamic noise is derived via the algorithm. A simulation example is given to show the effectiveness of the proposed algorithm. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society