Adaptive tracking control of leader–follower systems with unknown dynamics and partial measurements

In this paper, a decentralized adaptive tracking control is developed for a second-order leader–follower system with unknown dynamics and relative position measurements. Linearly parameterized models are used to describe the unknown dynamics of a self-active leader and all followers. A new distributed system is obtained by using the relative position and velocity measurements as the state variables. By only using the relative position measurements, a dynamic output–feedback tracking control together with decentralized adaptive laws is designed for each follower. At the same time, the stability of the tracking error system and the parameter convergence are analyzed with the help of a common Lyapunov function method. Some simulation results are presented to validate the proposed adaptive tracking control.     

线性多智能体系统一致性问题的部分稳定性方法（英文）

A linear transformation is proposed to deal with the consensus problem of high-order linear multi-agent systems(LMASs).In virtue of the linear transformation, the consensus problem is equivalently translated into a partial stability problem. We discuss three issues of the LMASs under a generalized linear protocol: 1) to find criteria of consensus convergence; 2) to calculate consensus function; 3) to design gain matrices in the linear consensus protocol. Precisely, we provide a necessary and sufficient criterion of consensus convergence in terms of Hurwitz stability of a matrix and give an analytical expression of the consensus function. In addition, we set up a relation between the gain matrices in the protocol and the convergence time and consensus accuracy of the agents, and then design the gain matrices with respect to a pre-specified convergence time and a required consensus accuracy.     

基于Wiener结构的软测量模型及辨识算法

Wiener模型结构能有效地表征系统的动态和静态特性, 因此这里首先基于这一结构建立软测量模型, 利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系, 并说明该软测量模型的可行性, 给出模型具体表达式. 其次, 针对所提模型, 提出分步辨识方式获得最优模型参数, 说明其可行性. 再次, 为了减少计算和实现模型在线更新, 这里提出参数辨识递推算法, 并给出软测量模型参数的收敛性结论. 通过实例仿真, 可以看出本文提出模型的可行性, 以及分步辨识方式与递推算法的有效性.     

一种SISO线性系统的系统辨识软件包的开发

本文基于系统辨识的最小二乘法,介绍一种用Basic语言编程的SISO线性系统系统辨识软件包及其原理、结构、功能与特点。它包括输入/输出数据的生成,近十种辨识程序模块的调用,结果的处理,模型阶次与纯滞后时间的确定和模型校验等五大部分。经应用已证明设计合理,方便可靠,并具有结构简单灵活等特点。     

AN ITERATIVE FILTERING ALGORITHM FOR NON-FOURIER FREQUENCY ESTIMATION

Abstract. We study an iterative filtering method to estimate frequencies of random Gaussian sinusoids in white noise. The method uses higher order crossings and takes advantage of a fixed point to guide the use of bandpass filtering in an attempt effectively to increase the signal-to-noise ratio. At each iteration the expected zero-crossing rate is estimated from independent time series. Convergence occurs with any prespecified probability less than one.     

Modeling for soft sensor systems and parameters updating online

Soft sensor technology is an important means to estimate important process variables in real-time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic modeling methods integrate the processes of building the dynamic and static relationships between secondary and primary variables, which limits the estimation accuracy for primary variables. To avoid the problem, a kind of soft sensor model consisting of a dynamic model in cascade with a static one is proposed. The model identification and update online are conducted in substep way. In order to improve the model update efficiency, two improved Gauss–Newton recursive algorithms, which avoid nonsingular covariance matrix, are proposed for time-invariant and time-variant soft sensor systems. The uniform convergence for dynamic model parameter and the existence of estimation deviations for static model parameters are proved for time-invariant soft sensor system. The parameters of time-variant soft sensor system would be boundedly convergent. Case study confirms that, on the basis of the proposed model and recursive algorithms, the dynamic and static characteristics of soft sensor system can be described efficiently, and the primary variables are ensured to be estimated accurately.     

改进蚁群算法在应急VRP中的应用及收敛性分析

应急条件(地震、泥石流、洪水等)下的路况不稳定,随时会发生意外的问题.为了解决应急条件下的物资分配,在研究蚁群算法解决传统VRP(vehicle routing problem)的基础上,通过加入动态路况子对VRP数学模型进行改进,提出蚁群算法对改进后的车辆路路径问题的数学模型,并利用陕西省21个城市的实际经纬度作为应急情况下的模拟货物需求地点和仓库进行系统仿真,对影响改进算法收敛性的参数进行分析.通过理论研究和MATLAB仿真,证明了这种改进的算法能够有效解决实际情况中的应急物流分配问题.     

基于局部自适应先验MRF模型的快速BP图像分割算法

针对常规MRF分割模型不能有效描述图像的局部特征、常导致图像的过分割现象,提出了一种局部自适应先验的MRF模型。该模型利用图像的邻接区域信息建立了一种局部自适应特征MRF模型。基于提出的模型,建立了一种具有快速收敛策略的区域BP算法对MRF模型的区域消息进行传递,有效解决了区域BP算法的计算量大的问题。实验结果表明,与常规区域BP算法相比,提出的分割方法具有更快的分割速度和精度。     

基于Radau伪谱法的非线性最优控制问题的收敛性

在过去的10年里,伪谱方法(如Legendre伪谱法、Gauss伪谱法、Radau伪谱法)逐步成为求解不同领域中非线性最优控制问题的一种高效、灵活的数值解法.本文从最优控制问题解的存在性、收敛性以及解的可行性3个方面对采用Radau伪谱法求解一般非线性最优控制问题解的收敛性进行研究.证明了原最优控制问题的离散解存在、存在收敛到原最优控制问题解上的离散解和离散形式的收敛解是原最优控制问题的最优解.在此基础上,证明了Radau伪谱法的收敛性.本文结论与现有文献相比,去掉了一些必要条件,更适合一般的非线性时不变系统.     

一种求解约束优化问题的改进型免疫克隆算法

针对约束优化问题提出一种基于精英库机制的改进型免疫克隆优化算法ICOAEB(Immune clonal optimization algorithm based on elite bank)。该算法利用精英库机制动态存储迭代过程中父代优势个体,实现优秀个体的多代记忆,从而提高算法寻优能力;并利用灾变算子扰动算法运行过程从而摆脱迭代缓慢的状态,避免局部收敛。通过对五个约束优化函数的测试,实验结果表明ICOAEB的求解精度和稳定性较高,可以较好地解决约束优化问题。最后针对影响算法性能的两项重要参数选择问题给出了相关的实验及分析。