基于马尔科夫拓扑切换和随机通信干扰的连续时间多自主体系统的趋同控制

研究了基于不可靠通信网络的连续时间多自主体系统的趋同控制.自主体间的通信信道受高斯噪声干扰;不可靠通信因素导致的网络拓扑随机切换由马氏链刻画.为克服随机噪声和马尔科夫拓扑切换的影响,设计了随机逼近型趋同协议;基于马氏跳参数随机微分方程稳定性理论、代数图理论、连续鞅和马氏链理论,证明了多自主体系统实现渐近无偏均方平均趋同...     

有限通信数据率下一阶多自主体系统的包含控制设计

研究了多个静态领导及有限通信数据率下一阶多自主体系统的分布式包含控制问题.通信拓扑为有向图.给出了存在基于量化信息的协议使得系统达到包含一致性的充要条件,还给出了此包含控制协议的设计算法.     

一类不确定随机非线性系统的采样扩张状态观测器设计与观测误差的收敛性分析

实际系统中普遍存在各种干扰和不确定性因素,并且对控制系统的性能造成负面影响,因此设计对干扰和不确定性具有优异估计性能的观测器显得尤为重要.针对一类具有采样输出的不确定随机非线性系统,设计相应的采样扩张状态观测器用于在线估计不可量测状态和影响系统性能的随机总干扰.所估计的随机总干扰包含系统内部未建模动态、统计特性未知的外部有界噪声干扰以及不确定性因素的非线性耦合作用.在每个采样区间内,设计一个输出预估器用于估计实际输出,相应的输出估计值用于采样扩张状态观测器的设计.证明了所设计的采样扩张状态观测器对不可量测状态和随机总干扰的观测误差的均方收敛性.最后,一个具体仿真实例用于证实理论结果.     

网络系统的控制与优化

<正>近年来,网络系统的控制与优化是国内外科学工作者重点关注的研究领域,该项研究与生物、社会系统和现代工程技术的发展密切相关.多智能体系统是一类具代表性的网络系统,它可普遍用于多机器人的编队控制、空间无人机的分布式队形控制,也可为传感器网络研究以及复杂系统的自主协调控制等提供必要的理论基础.其中的一个典型问题在于理解如何通过仅调节少量个体来控制整个网络系统的行为,这是对网络系统施加控制的一种     

具有随机不确定通讯连接和马氏切换拓扑的多自主体系统的鲁棒H∞领导跟踪一致性研究

文章研究了马氏切换拓扑下多自主体系统的鲁棒H∞领导跟踪一致性问题.由于通讯环境中难以预知的变化,自主体之间的通讯连接可能具有随机的不确定成分,文章用伯努利分布序列来描述这种随机不确定性.同时,在很多实际系统中,想精准获得马氏跳变过程的转移率矩阵中的元素是不容易的,或者获取成本很高,因此文章中马氏切换拓扑的转移率是假定为...     

基于电改背景下的风光燃储微电网参与现货市场交易的决策优化模型研究

自新一轮电改以来,市场主体培育已初见成效,现货市场试点的开展指明了我国电力市场化交易机制改革的方向.与此同时,分布式能源发电技术和微电网能源管理技术日益成熟,在含分布式电源的微电网运营商如何参与现货市场交易的问题已引起广泛关注.在考虑需求响应和风光出力不确定性的基础上,以包含风电、光伏、燃气和储能系统(风光燃储)的微电网为研究对象,在现货市场环境下,建立了以微电网运营商参与现货市场交易收益最大化为目标的决策优化模型,并通过算例仿真验证了模型的有效性和合理性.     

网络化Euler-Lagrange系统的分布式编队机动控制北大核心CSCD

研究了网络化Euler-Lagrange系统自适应编队机动控制问题.针对参数不确定的Euler-Lagrange系统,利用滑模控制方法提出了一种自适应编队机动控制算法.基于Lyapunov稳定性理论,证明了闭环系统的稳定性.该算法的显著特点是通过引入一种特殊的有向网络拓扑来描述智能体之间的通信交互行为,使得系统中跟随者在不需要知道或估计时变机动参数的情况下,能够实现编队的方向、平移、形状的连续改变.最后对提出的自适应编队机动控制算法进行数值模拟以验证该控制方案的有效性.     

多自主体系统的分析与干预

多自主体系统集体行为的研究可以分为三类:分析,设计和干预.针对经典的群集模型-Vicsek模型及其相关模型,介绍了系统控制重点实验室近年来取得的研究成果和进展,主要包括概率框架下Vicsek模型的同步分析,以及针对干预问题提出的软控制等方法在Vicsek模型同步问题和多人博弈系统合作问题中的应用.     

函数空间多体挠性结构系统动力学、稳定性与控制的研究

利用现代数学方法,在函数空间中研究了一类无穷维系统动力学、稳定性与控制问题.首先提出并建立了具有阻尼、陀螺部件和约束阻尼的多拓扑结构多挠体分布参数系统动力学控制模型;其次给出并论证了多体挠性结构特征、系统分析结果--可控可观性充要条件、稳定性理论和系统的渐近性质.研究的结果扩充和发展了本领域关于多挠体系统动力学与控制的理论成果,具有重要的工程意义.     

基于事件驱动的环形编队多智能体系统

研究了控制器采用事件驱动机制时,多智能体系统的环形编队控制问题.事件驱动控制机制用来降低个体控制更新的频率,以此减少整个系统的能量消耗.假设所有智能体始终在一固定的圆环上运动,并且每个智能体只能感知到其前后两个紧邻智能体的信息.利用邻居的信息,对每个智能体设计分布式控制律,在事件驱动控制机制下,所提控制策略可以使多智能体系统形成给定的期望环形编队,并将环形编队扩展到特殊的均匀环形编队形式.数值的仿真验证了所提控制策略的有效性.     

Distributed impulsive control consensus for a class of unknown nonlinear multi-agent systems based on event-triggered scheme

In this study, we are concerned with the impulsive consensus control problem for a class of nonlinear multi-agent systems (MASs) which have unknown dynamics and directed communication topology. The neural networks (NNs) method is the first utilized to construct distributed event-triggered impulsive consensus protocol. In contrast to the existing impulsive consensus protocol, the consensus protocol proposed in this paper does not need the dynamics of agents, which enhances the system robustness, and realizes distributed event-triggered communication between agents, which can reduce unnecessary consumption of communication resources. Sufficient conditions are derived to ensure the consensus of the controlled MASs and the exclusion of Zeno-behavior. Finally, simulation examples are presented to illustrate the effectiveness of the proposed control protocol.     

Imprecise parameters for near-optimal control of stochastic SIV epidemic model

The change of parameters may influence the dynamic behaviors of epidemic diseases. Biological system parameters can also be changed due to diverse uncertainties such as lack of data and errors in the statistical approach. The problem of how to define and decide the optimal-control strategies of epidemic diseases with imprecise parameters deserves further researches. The paper presents a stochastic susceptible, infected, and vaccinated (SIV) system that includes imprecise parameters. Firstly, we give the method of parameter estimates of the SIV model. Then, by using Ekeland's principle and Hamiltonian function, we obtain the sufficient conditions and necessary conditions of near-optimal control of the SIV epidemic model with imprecise parameters. At last, numerical examples prove our theoretical results.     

Control by Interconnection and Energy Shaping of the Timoshenko Beam

In this paper, the dynamical control of a mixed finite and infinite dimensional mechanical system is approached within the framework of port Hamiltonian systems. In particular, a flexible beam, modeled according to the Timoshenko theory and in distributed port Hamiltonian form, with a mass under gravity field connected at a free end, is considered. The control problem is approached by generalization of the concept of structural invariant (Casimir function) to the infinite dimensional case and the so-called control by interconnection technique is extended to the infinite dimensional case. In this way, finite dimensional passive controllers can stabilize distributed parameter systems by shaping their total energy, i.e., by assigning a new minimum in the desired equilibrium configuration that can be reached if a dissipation effect is introduced.     

Controllability and stabilizability of linear time‐varying distributed hereditary control systems

This paper is concerned with the controllability and stabilizability problem for control systems described by a time‐varying linear abstract differential equation with distributed delay in the state variables. An approximate controllability property is established, and for periodic systems, the stabilization problem is studied. Assuming that the semigroup of operators associated with the uncontrolled and non delayed equation is compact, and using the characterization of the asymptotic stability in terms of the spectrum of the monodromy operator of the uncontrolled system, it is shown that the approximate controllability property is a sufficient condition for the existence of a periodic feedback control law that stabilizes the system. The result is extended to include some systems which are asymptotically periodic. Copyright © 2014 John Wiley & Sons, Ltd.     

广义分布参数系统的状态反馈稳定性问题(1)

关于系统的状态反馈稳定性问题的研究一直是现代控制理论研究的重要问题之一.广义分布参数系统是比分布参数系统更广的一类系统,在研究复合材料热导体中的温度分布等问题时会出现这样的系统.本文讨论了H ilbert空间中一阶广义分布参数系统的状态反馈稳定性问题.应用泛函分析及线性算子半群理论的方法给出了使闭环广义分布参数系统渐进稳定的充要条件,充分条件及状态反馈的构造性表达式.这对研究广义分布参数系统的状态反馈稳定性问题具有重要的理论价值.     

Delay‐dependent robust reliable control for uncertain switched neutral systems

This article investigates the robust reliable control problem for a class of uncertain switched neutral systems with mixed interval time‐varying delays. The system under study involves state time‐delay, parameter uncertainties and possible actuator failures. In particular, the parameter uncertainties is assumed to satisfy linear fractional transformation formulation and the involved state delay are assumed to be randomly time varying which is modeled by introducing Bernoulli distributed sequences. The main objective of this article is to obtain robust reliable feedback controller design to achieve the exponential stability of the closed‐loop system in the presence of for all admissible parameter uncertainties. The proposed results not only applicable for the normal operating case of the system, but also in the presence of certain actuator failures. By constructing an appropriate Lyapunov–Krasovskii functional, a new set of criteria is derived for ensuring the robust exponential stability of the closed‐loop switched neutral system. More precisely, zero inequality approach, Wirtinger's based inequality, convex combination technique and average dwell time approach are used to simplify the derivation in the main results. Finally, numerical examples with simulation result are given to illustrate the effectiveness and applicability of the proposed design approach. © 2015 Wiley Periodicals, Inc. Complexity 21: 224–237, 2016     

Autonomous control of complex systems: robotic applications

One of the biggest challenges of any control paradigm is being able to handle large complex systems under unforeseen uncertainties. A system may be called complex here if its dimension (order) is too high and its model (if available) is nonlinear, interconnected, and information on the system is uncertain such that classical techniques cannot easily handle the problem. Soft computing, a collection of fuzzy logic, neuro-computing, genetic algorithms and genetic programming, has proven to be a powerful tool for adding autonomy to many complex systems. For such systems the size soft computing control architecture will be nearly infinite. Examples of complex systems are power networks, national air traffic control system, an integrated manufacturing plant, etc. In this paper a new rule base reduction approach is suggested to manage large inference engines. Notions of rule hierarchy and sensor data fusion are introduced and combined to achieve desirable goals. New paradigms using soft computing approaches are utilized to design autonomous controllers for a number of robotic applications at the ACE Center are also presented briefly.     

Distributed adaptive switching consensus control of heterogeneous multi-agent systems with switched leader dynamics

In this paper, the leader-following distributed consensus control problem is addressed for general linear multi-agent systems with heterogeneous uncertain agent dynamics and switched leader dynamics. Different from most existing results with a single linear time-invariant (LTI) leader dynamics, the leader dynamics under consideration is composed by a family of LTI models and a switching logic governing the switches among them, which is capable of generating more diverse and sophisticated reference signals to accommodate more complicated consensus control design tasks. A novel distributed adaptive switching consensus protocol is developed by incorporating the model reference adaptive control mechanism and arbitrary switching control technique, which can be synthesized by following a two-layer hierarchical design scheme. A numerical example has been used to demonstrate the effectiveness of the proposed approach.     

Exact minimum-time control of a distributed system using a traveling wave formulation

In this paper, the minimum-time control problem for rest-to-rest translation of a one-dimensional second-order distributed parameter system by means of two bounded control inputs at the ends is solved. A traveling wave formulation allows the problem to be solved exactly, i.e., without modal truncation. It is found that the minimum-time control is not bang-bang, as it is for systems with a finite number of degrees of freedom. Rather, it is bang-off-bang, where a period of control inactivity in the middle of the control time interval is required for synchronization with waves propagated through the system.This research was supported in part by AFOSR Grant No. AFOSR-90-0297. The helpful suggestions of the referees are gratefully acknowledged.     

Adaptive control of linear stochastic evolution systems

An adaptive control problem for some linear stochastic evolution systems in Hilbert spaces is formulated and solved in this paper. The solution includes showing the strong consistency of a family of least squares estimates of the unknown parameters and the convergence of the average quadratic costs with a control based on these estimates to the optimal average cost. The unknown parameters in the model appear affinely in the infinitesimal generator of the C ₀ semigroup that defines the evolution system. A recursive equation is given for a family of least squares estimates and the bounded linear operator solution of the stationary Riccati equation is shown to be a continuous function of the unknown parameters in the uniform operator topology