基于模糊竞争学习的非线性系统自适应模糊建模方法

提出了一种新的基于模糊竞争学习的自调整的模糊建模方法. 基于模糊竞争学习, 模糊系统能够进行自适应模糊推理. 在被调整模糊系统基础上, 提出了一种非线性系统在线估计参数的在线辨识算法. 为了证明提出算法的有效性, 最后给出了几个例子的仿真结果.     

基于模糊分类的模糊神经网络辨识方法及应用

基于改进的T-S模型，提出一种自适应模糊神经网络模型(AFNN)，给出了网络的连接结构和学习算法。基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。利用卡尔曼滤波算法在线辨识删的后件参数。AFNN结构简洁，逼近能力强，能够显著提高辨识精度，并且在线辨识的模糊模型简单有效。将该AFNN用于非线性系统的模糊辨识和化工过程连续搅拌反应器(CSTR)的建模中，仿真结果验证了该方法的有效性，表明该网络能够实现复杂非线性系统的建模，而且建模精度高、收敛速度快。可当作复杂系统建模的一种有效手段。     

基于模糊模型的结构和参数的一体化辨识

基于模糊集合的模糊建模捕述复杂、病态、非线性系统的特性是一种有效方法．文中讨论了从样本数据中通过正交变换和模糊聚类获取模糊规则的方法．利用正交最小二乘对模糊聚类的结果进行变换，采用CGS（Classical Gram—Schmidt）方法确定对建模贡献大的规则，删除对建模贡献小的规则，并对模型中的参数进行估计，能够同时模对糊模型的结构和参数进行辨识．仿真结果表明，提出的方法能够对非线性系统进行模糊建模．     

一种广义模糊神经网络的参数解耦学习算法

对于强非线性系统采用分段建模十分有效,广义模糊神经网络能实现这种思想。在此基础上,给出一种模糊规则前、后件参数可分别进行学习的算法,仿真结果表明该方法拟合能力强、学习效率高。     

基于模糊神经网络的非线性系统模型的辨识

该文提出一种非线性系统的模型辨识方法．利用关系聚类法来进行结构辨识，从而自动获得模糊规则库，并可以得到模糊系统的初始参数，在聚类的基础上，构造一个与之相匹配的模糊神经网络，用它的学习算法来训练网络，得到一个精确的模糊模型，从而实现参数辨识，通过对两个非线性系统辨识的仿真结果验证了该方法的有效性。     

基于模糊规则的非线性系统建模方法

提出了一种基于模糊聚类自调整的模糊建模方法,基于模糊聚类通过自适应模糊推理来调整模糊系统,一种在线辨识算法的是通过非线笥系统参数的在线性估计来进行的,为了证明了所提出方法的适用性,给出了几个实例的仿真结果。     

基于模糊神经网络的系统辨识

基于模糊神经网络研究系统辨识问题,提出一种具体的模糊神经网络结构和相应算法,设计了开环系统和闭环系统辨识的结构。针对多个不同的对象进行仿真研究,结果表明用模糊神经网络建模较之传统建模方法能力强。     

基于T S模型的模糊系统辨识方法综述*

模糊模型设计方法归结为两种,即语义驱动和数据驱动。数据驱动模型具有更好的性能,是目前研究的热点。模糊系统辨识是数据驱动下模糊系统建模的重要手段,辨识的优良直接影响系统建模的精度。模糊系统辨识可以分为两部分进行认识,即模糊系统结构辨识和参数辨识。回顾了近年来模糊系统辨识的理论和方法,如subtractive聚类、多分辨率自适应空间分解、SVM、核函数法、粒子群算法和并行遗传算法等。对各种算法原理、特点进行了介绍,对模糊系统辨识的发展进行了展望。     

基于T S模型的模糊系统辨识方法综述*

模糊模型设计方法归结为两种,即语义驱动和数据驱动。数据驱动模型具有更好的性能,是目前研究的热点。模糊系统辨识是数据驱动下模糊系统建模的重要手段,辨识的优良直接影响系统建模的精度。模糊系统辨识可以分为两部分进行认识,即模糊系统结构辨识和参数辨识。回顾了近年来模糊系统辨识的理论和方法,如subtractive聚类、多分辨率自适应空间分解、SVM、核函数法、粒子群算法和并行遗传算法等。对各种算法原理、特点进行了介绍,对模糊系统辨识的发展进行了展望。     

动态系统模糊辨识的新算法*

本文针对复杂动态系统的辨识问题，提出了一种基于一类标准模糊系统的模糊辨识的简单学习算法。仿真研究表明该算法具有辨识精度高、所需样本量小以及运算速度快等优点，是动态系统模糊辨识的有效工具。     

连续大系统的网络结构最优滤波器

本文提出一种连续大系统状态估计器的设计方法--网络结构最优滤波器.这个方法基 于矩阵最小值原理,其计算结果是满足任意结构约束的最优估计.对于分散和递阶结构而言, 此方法具有容错和设计灵活等特点,特别适宜于用多计算机系统来实现,且不要求信道有很宽 的通频带.     

基于模态逻辑的网络测量策略模型

当前大规模网络测量系统中不维护状态,因此也很难支持可靠的协作测量.针对这一问题提出了一种基于模态逻辑的网络测量策略模型(ML-NMPM),定义了基于模态逻辑的策略语言和执行模型. ML-NMPM将大规模分布式系统分成自治管理的对等监测域,通过策略代理维护资源状态信息形成全局状态视图,从而支持基于系统状态的协作测量.提高了测量系统的灵活性、可靠性和协作能力.最后分析了ML-NMPM的性能并给出原型系统.     

分布式电网CPS 系统数据攻击下的状态估计

电力物理网络通过构建信息网络进行优化调控并构成信息物理融合系统, 实现大规模分布式系统的优化控制, 随之而来的问题是病毒、黑客入侵、拒绝服务等来自信息网络的威胁, 导致物理系统恶意破坏. 鉴于此, 以攻击可检测为前提, 建立攻击信号下的电力系统分布式动态模型, 设计动态状态估计器检测受攻击的信号, 并估计其原始信号. 最后通过3 机9 节点分布式电网系统仿真实验验证了所设计的状态估计器对于数据攻击检测的有效性.

     

Decentralized control: Status and outlook

This paper reviews state of the art in the area of decentralized networked control systems with an emphasis on event-triggered approach. The models or agents with the dynamics of linear continuous-time time-invariant state-space systems are considered. They serve for the framework for network phenomena within two basic structures. The I/O-oriented systems as well as the interaction-oriented systems with disjoint subsystems are distinguished. The focus is laid on the presentation of recent decentralized control design and co-design methods which offer effective tools to overcome specific difficulties caused mainly by network imperfections. Such side-effects include communication constraints, variable sampling, time-varying transmission delays, packet dropouts, and quantizations. Decentralized time-triggered methods are briefly discussed. The review is deals mainly with decentralized event-triggered methods. Particularly, the stabilizing controller–observer event-based controller design as well as the decentralized state controller co-design are presented within the I/O-oriented structures of large scale complex systems. The sampling instants depend in this case only on a local information offered by the local feedback loops. Minimum sampling time conditions are discussed. Special attention is focused on interaction-oriented system architecture. Model-based approach combined with event-based state feedback controller design is presented, where the event thresholds are fully decentralized. Finally, several selected open decentralized control problems are briefly offered as recent research challenges.     

Consensus based overlapping decentralized estimation with missing observations and communication faults

In this paper a new algorithm for discrete-time overlapping decentralized state estimation of large scale systems is proposed in the form of a multi-agent network based on a combination of local estimators of Kalman filtering type and a dynamic consensus strategy, assuming intermittent observations and communication faults. Under general conditions concerning the agent resources and the network topology, conditions are derived for the convergence to zero of the estimation error mean and for the mean-square estimation error boundedness. A centralized strategy based on minimization of the steady-state mean-square estimation error is proposed for selection of the consensus gains; these gains can also be adjusted by local adaptation schemes. It is also demonstrated that there exists a connection between the network complexity and efficiency of denoising, i.e., of suppression of the measurement noise influence. Several numerical examples serve to illustrate characteristic properties of the proposed algorithm and to demonstrate its applicability to real problems.     

一种基于动态规划策略的离散动态大系统递阶优化神经网络

给出了一种具有集成化特征的、快速求解大规模系统动态规划问题的神经网络模型 (LDPNN),该神经网络将大系统的各子系统的动态方程约束嵌入局部优化子网络,使得整个 网络的结构简洁、紧凑,便于硬件实现,该神经网络计算模型克服了数值方法迭代计算的缺 陷,求解效率高,适宜于大规模动态系统实时优化应用.     

Progress in computational physics

Recent progress in computational methods for time dependent fluid dynamics is presented. The emphasis is on advances applicable to large scale systems with the connection between the numerics and the physics of the code stressed. All aspects of a working code are discussed including such topics as initialization, boundary conditions, grid generation in addition to algorithmic advances. One sometimes uses a time dependent method as an iteration procedure to reach a steady state solution. Work in accelerating the convergence to the steady state is also surveyed.     

Target mass control for uncertain compartmental systems

In this article we analyse the total mass target control problem for compartmental systems under the presence of parameter uncertainties. We consider a state feedback control law with positivity constraints tuned for a nominal system, and prove that this law leads the value of the total mass of the real system to an interval whose bounds depend on the parameter uncertainties and can be made arbitrarily close to the desired value of the total mass when the uncertainties are sufficiently small. Moreover, we prove that for a class of compartmental systems in ?³ of interest, the state of the controlled system tends to an equilibrium point whose total mass lies within the aforementioned interval. Taking into account the relationship between the mass and the state components in steady state, it is possible to use the proposed mass control law to track the desired values for the steady state components. This is applied to the control of the neuromuscular blockade level of patients undergoing surgery, by means of the infusion of atracurium. Our results are illustrated by several simulations and a clinical case.     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.     

一类不确定非线性大系统的分散鲁棒控制器设计

基于Backstepping方法，设计了一类具有不确定性扰动和不确定性关联项的非线性大系统的分散鲁棒稳定控制器。非线性大系统的关联项为时变有界非线性函数且不确定性扰动以仿射非线性方程的形式引入。为了提高系统的控制效果。将：Backstepping递推设计方法与L2增益控制相结合，所设计的分散鲁棒控制器不仅使每个子系统的状态向量跟踪一个指定的期望轨迹。而且还使系统的不确定性干扰具有L2增益控制。