基于模型的网络控制系统稳定性

针对具有时间延迟的网络控制系统被控对象状态无法直接测量得到的情况，设计了状态观测器，并讨论了系统指数稳定性问题．提出了在网络传输信号的时间间隔内依据被控对象模型计算控制信号的开环控制方法，以减少对网络的使用．在此基础土，分别对连续和离散被控对象给出了网络控制系统全局指数稳定的充要条件，该条件受网络信号更新时间、被控对象模型误差及网络引起的时间延迟等因素的影响．仿真示例验证了稳定性条件的有效性。     

基于观测器的网络控制系统的故障检测

研究了具有输出传输长时延的网络控制系统基于观测器的故障检测,在总结其设计方法的基础上利用预测状态作为控制器的输入,设计了具有延迟补偿功能的状态观测器,通过求取观测器增益矩阵来使得状态重构能跟上实际状态的响应性能。产生的残差信号对干扰信号和不确定项具有较强的鲁棒性,同时对故障信号具有较高的灵敏度。最后给出了网络控制系统基于观测器的故障检测的设计步骤。     

网络控制系统优化设计研究

研究网络控制系统的优化问题,提高系统的实时性。针对传统的网络控制系统是闭环系统,网络带宽有限,网络传输中的时间延迟、数据包丢失等问题不可避免。当发生以上情况时,反馈信号不能及时传回,造成控制信号发出延迟,影响了系统的实时性。为了解决上述问题,提出了一种非规律时滞补偿算法,把时滞信号引入自由权矩阵,能够得到延迟信号相关稳定性条件,并充分运用线性信号特征矩阵保证网络控制系统的性能,保证系统在线控制的实时性。证明改进方法能够避免控制信号延迟造成的滞后问题,提高了网络控制系统的实时性。     

预估网络控制系统的设计和分析

针对网络控制系统中存在的随机掉包和延迟情况．采用前向和反馈补偿设计网络状态观测器．并运用状态预估的方法设计网络预估控制系统（PNCS）。作为网络控制系统中的掉包和延迟的整体解决方案．同时介绍PNCS系统各部分设计的方法，并给出了闭环系统的稳定性条件．最后通过一个仿真算例验证了该系统的有效性．     

一类基于模型的时延网络控制系统的稳定性分析

针对连续、线性被控对象状态无法直接测量得到的情况，设计了状态观测器，在网络传输信号的时间间隔内，依据被控对象模型计算控制信号，以减少网络的使用率．在此基础上，给出了一类基于模型的时延网络控制系统全局指数稳定的充要条件，该条件受网络信号更新时间、被控对象模型误差及网络诱导时延等因素的影响．仿真结果表明了该稳定性条件的正确性．     

具有状态观测器的网络化控制系统的设计

研究存在时变延迟和丢包的非理想网络传输情况下带状态观测器的网络化控制系统设计问题.提出一种基于分级控制结构的、具有本地状态观测器的网络化控制系统的设计方法,证明了基于分级控制结构的网络化控制系统满足分离定理的充分条件.该方法在降低系统设计难度的同时,有效改善了网络化控制系统的控制性能.数值仿真表明了所提出方法的有效性.     

基于延迟观测器的采煤机自动调高策略研究

针对采煤机调高系统中信号存在的输出延迟问题,对系统控制精度造成的影响,提出基于输出延迟观测器的滑模控制策略.通过分析采煤机自动调高系统的工作原理,建立采煤机滚筒高度和调高油缸数学模型,得到了采煤机滚筒高度和系统空间状态方程,设计了输出延迟观测器,分析了不同延迟时间内的观测误差,采用滑模控制器实现了采煤机滚筒高度的自动调节.仿真结果表明,将延迟观测器与滑模控制相结合,不仅解决了信号输出存在的延迟问题,而且相对于无延迟观测器的控制系统,跟踪误差小、控制效果理想.     

网络控制系统的增广状态极点配置设计法

参数时变系统可以通过增广状态法转换成一种标准形式．本文应用极点配置设计法，设计了基于网络控制系统标准形式模型的控制规律和观测器，并证明了分离定理仍然成立．仿真研究表明该设计方法应用到网络控制系统的有效性．     

基于Internet控制系统延迟的一种解决方法

介绍了基于Internet的网络控制系统的组成及最近的研究状态，对于系统中网络时延这个重要问题进行了分析，通过分析提出了一种针对延迟的动态重发机制的解决方法，在被控端进行延迟时刻规律的完整学习，并且把此规律传给控制端。控制端依照此规律，在延迟发生概率较大的时刻进行瞬时重发，并不断得进行周期性得学习。从而降低了网络延迟情况．     

基于模型的网络控制系统稳定性分析及应用

针对时延网络控制系统中被控对象状态无法直接测量的情况，提出了基于模型的时延网络控制系统模型。系统采用状态反馈控制器，在网络信号传输时间间隔内，以被控对象模型为依据，计算控制信号，以减少系统对网络的依赖。在此基础上，给出了网络控制系统全局指数稳定的充要条件，该条件受网络信号更新时间、被控对象模型误差及网络引起的时延等因素的影响。仿真示例验证了该条件的有效性，应用在工业控制系统中，取得了较好的控制效果。     

基于深度强化学习的交通信号控制方法

交通信号的智能控制是智能交通研究中的热点问题。为更加及时有效地自适应协调交通,文中提出了一种基于分布式深度强化学习的交通信号控制模型,采用深度神经网络框架,利用目标网络、双Q网络、价值分布提升模型表现。将交叉路口的高维实时交通信息离散化建模并与相应车道上的等待时间、队列长度、延迟时间、相位信息等整合作为状态输入,在对相位序列及动作、奖励做出恰当定义的基础上,在线学习交通信号的控制策略,实现交通信号Agent的自适应控制。为验证所提算法,在SUMO(Simulation of Urban Mobility)中相同设置下,将其与3种典型的深度强化学习算法进行对比。实验结果表明,基于分布式的深度强化学习算法在交通信号Agent的控制中具有更好的效率和鲁棒性,且在交叉路口车辆的平均延迟、行驶时间、队列长度、等待时间等方面具有更好的性能表现。     

Development of performance model for calculation of communication delay in Profibus token passing protocol

In many automated systems such as manufacturing systems and process plants, a fieldbus is a very important component for the exchange of various and sometimes crucial information. Some of the information has a tendency to rapidly lose its value as time elapses after its creation. Such information or data is called real-time data that includes sensor values and control commands. In order to deliver these data in time, the fieldbus network should be tailored to have short delay with respect to the individual time limit of various data. Fine-tuning the network for a given traffic requires knowledge on the relationship between protocol parameters such as timer values and performance measure such as network delay. This paper presents a mathematical performance model to calculate communication delays of the Profibus FMS network when the timer value and the traffic characteristics are given. The results of this model are compared to those from experiments to assess the model's validity.     

Motion Synchronization Control of Distributed Multisubsystems With Invariant Local Natural Dynamics

This paper addresses a new control strategy for synchronizing two or more distributed and interconnected dynamic systems having communication time delays. The proposed strategy that uses the Smith predictor principle and delay information not only achieves synchronization but also preserves the natural local dynamics of each subsystem without being affected by the feedback nature of control. The proposed synchronization scheme is generalized to cases that deal with an arbitrary number of heterogeneous interconnected systems through dynamic scaling of input under a ring-type network configuration. In addition, possibility of applying the proposed scheme to nonlinear systems is discussed. Simulation and experimental tests are conducted to validate theoretical results.     

基于延时预测的模型状态反馈网络控制方法

网络延时的存在不仅会使控制系统的动、静态性能变差,还可能会破坏系统的稳定性,造成既有控制方法的失效。针对带有随机延时的网络控制系统,提出了一种基于网络延时实时预测和系统状态逼近的控制方法。首先,建立无延时的理想控制系统模型作为标准控制系统模型;其次,应用基于隐马尔可夫模型(HMMs)的延时预测算法预测网络延时,该方法能够实时自适应学习,以适应网络负载情况的变化,得到更符合实际情况的延时预测值;最后,根据网络延时的预测值、实际网络控制系统与标准控制系统模型的状态之差,来调整控制信号,从而使实际网络控制系统的状态与标准控制系统模型接近、甚至相等,达到期望的控制效果。数字仿真结果表明,这一方法是可行的,能够获得更加快速的动态性能和稳定的静态性能。     

网络时延的模糊逻辑补偿方法的研究

当网络应用到控制系统中时，网络将引起时延，从而对闭环网络控制系统产生一些不利的影响，比如系统性能下降，系统不稳定等。本文介绍了通过在已有的PI控制器的基础上，再增加一个模糊逻辑补偿器来补偿网络控制系统中网络所引起的时延，其优点是不需要再重新设计已有的PI控制器，而只是简单地将模糊逻辑控制器的输出作为一个参数来调节PI控制器所提供的控制信号。文中采用了MATLAB／SIMULINK仿真，仿真结果表明了该方法的有效性。     

网络时延的模糊逻辑补偿方法的研究

当网络应用到控制系统中时,网络将引起时延,从而对闭环网络控制系统产生一些不利的影响,比如系统性能下降,系统不稳定等。本文介绍了通过在已有的PI控制器的基础上,再增加一个模糊逻辑补偿器来补偿网络控制系统中网络所引起的时延,其优点是不需要再重新设计已有的PI控制器,而只是简单地将模糊逻辑控制器的输出作为一个参数来调节PI控制器所提供的控制信号。文中采用了MATLAB/SIMULINK仿真,仿真结果表明了该方法的有效性。     

Cooperative tracking control for general linear multiagent systems with directed intermittent communications: An artificial delay approach

This paper investigates the consensus tracking problem for general linear multiagent systems on directed graph containing a spanning tree. For the considered linear systems, the consensus tracking aim cannot be achieved by using only memoryless static relative output feedbacks. Of particular interest is that both current and delayed relative output information of agents are required to achieve consensus. For the case of continuous communication among agents, an artificial delay output feedback control method is proposed. By utilizing the Taylor representation for the delayed signal with the remainder in the integral form, a delay‐dependent sufficient condition is presented to guarantee the exponential convergence of the global tracking error systems. For the intermittent case, the consensus tracking performance can still be guaranteed based on a multiple graph‐dependent Lyapunov functionals method. It is theoretically revealed that the time delay plays a key role in the exponential convergence of the closed‐loop systems, and the definite relationships among the time delay, network structure, communication rate, and consensus convergence rate are also provided. The effectiveness of the proposed control scheme is confirmed by numerical simulations.     

Stability and Stabilization of Networked Control Systems Under Limited Communication Capacity and Variable Sampling

This paper investigates the problem of quantized feedback control for networked control systems (NCSs) with time‐varying delays and time‐varying sampling intervals, wherein the physical plant is a continuous‐time, and the control input is a discrete‐time signal. By using an input delay approach and a sector bound method, the network induced delays, the signal quantization and sampling intervals are presented in one framework in the case of the state and the control input by quantization in a logarithmic form. We exploit a novel Lyapunov functional with discontinuity, taking full advantage of the NCS characteristic information including the bounds of delays, the bounds of sampling intervals and quantization parameters. In addition, it has been shown that the Lyapunov functional is decreased at the jump instants. Furthermore, we use the Leibniz‐Newton formula and free‐weighting matrix method to obtain the stability analysis and stabilization conditions which are dependent on the NCS characteristic information. The proposed stability analysis and stabilizing controller design conditions can be presented in term of linear matrix inequalities, which have less conservativeness and less computational complexity. Four examples demonstrate the effectiveness of the proposed methods.     

遥操作机器人系统的变时延控制

由于遥操作机器人传输通道中存在着变化的通讯时延,造成了系统不稳定和操作性能降低等问题。为了消除或减少时延的影响,在利用RBF神经网络进行延时预测的基础上,通过改进型Smith预估器进行遥操作机器人的时延控制,从而使系统稳定,并易于操作。理论分析表明,该方案对时延的变化有较强的适应性,控制系统的鲁棒稳定性和动态品质均优于单纯的Smith预估补偿控制,且易于实现。仿真结果表明,在时延情况下,该方法可实现对遥操作机器人较为准确的测控。     

Prediction and congestion control algorithm for networked motion tracking

In this paper a novel prediction method and a communication protocol is proposed for distributed motion tracking systems, for example robot control system over the Internet based on on-line visual information. It is assumed that the trajectory generator part of the control system is connected to the low level controller through wide area network (WAN). In this case the variable network delay, packet losses, irregular packet arrival can severely influence the control characteristics (transient behavior and tracking performance) in a negative sense. The proposed prediction method is based on dynamic filters and it generates the trajectory on the control system side in the control periods when no new information on the time varying reference trajectory arrives through the network. The developed application level communication protocol is meant to keep the packet loss under a predefined limit even if the network bandwidth varies below the value required by the control application. Simulations and real-time experiments show that the prediction algorithm applied jointly with the proposed communication protocol can effectively compensate the effect of networked communication on control characteristics.