网络控制系统的时延仿真研究与控制器设计

针对网络控制系统(NCS)中的随机时延问题,根据实际网络时延的分布情况,提出了一种新的具有随机时延的网络控制系统的建模方法-离散T-S模型,并用并行分布补偿原理(PDC)设计模糊控制器.同时提出一种新的模糊控制系统隶属函数的确定方法,利用Lyapunov定理和线性矩阵不等式(LMI)研究了系统的稳定性问题,给出基于LMI的模糊控制器的设计方法.最后通过仿真实例证明控制方法能够使具有时延的网络控制系统稳定.     

动态矩阵控制在网络时延补偿中的应用研究

在网络控制系统中,随机传输时延大大降低了系统的控制性能,并导致系统的不稳定.针对以太网在网络控制系统中的随机传输时延问题,提出了一种基于DMC的NCS时延补偿控制方案.该方案采用DMC算法设计控制器,同时通过网络时延补偿器和动态缓冲器整定控制量,使系统输出能迅速跟踪突变输入.利用TureTime工具箱进行仿真实验,结果证明了该算法在处理网络控制系统的时延问题时具有有效性和可行性.     

基于GPC的NCS非整数倍采样周期时延补偿方法

研究具有非整数倍采样周期时延的网络控制系统(NCS)的时延补偿问题.针对NCS中的非整数倍采样周期时延,基于广义预测控制算法并结合线性插值方法,提出一种新的网络时延补偿方法.该方法能得到非整数倍采样周期的控制预测值,同时通过减小执行器读取缓冲区的周期,可有效减少由于执行器时间驱动而引起的等待时延,解决了非整数倍采样周期时延的补偿问题.最后通过仿真验证了该方法的有效性.     

基于极点配置与时延误差补偿的网络控制系统预测控制

根据网络控制系统的时延上界及对象模型,建立网络控制系统的整体模型,应用极点配置广义预测方法进行控制器的设计,以保证网络控制系统控制的有效性与稳定性．考虑到网络时延的随机性,基于神经网络建立网络时延误差的预测补偿模型,用时延误差预测值对输出预测值进行补偿,构成基于极点配置和误差补偿的网络控制系统预测控制算法．仿真结果表明,本文方法有较高精度,可快速收敛．     

基于时延估计组合模型的NCS控制方法研究

在对网络传输时延的组成和特性进行分析的基础上,针对双边网络控制系统,提出了一种时延估计组合模型,并对该组合模型的有效性进行了评估。利用该模型进行网络时延估计,估计结果用来设计网络时延补偿控制器,以实际网络时延为实验数据进行了仿真实验,实验结果表明网络控制系统的动态性能有了明显改善,同时进一步证实了时延估计组合模型的有效性。     

基于网络控制系统平均时延的模糊控制器设计

针对网络控制系统(NCS)中的随机时延问题,根据实际网络时延的分布情况,提出一种新的具有随机时廷的网络控制系统的建模方法--离散T-S模型,并在此基础上应用并行分布补偿原理(PDC)设计模糊控制器.同时提出一种新的模糊控制系统隶属函数的确定方法,利用Lyapunov定理和线性矩阵不等式(LMI)研究系统的稳定性问题,给出了基于LMI的模糊控制器的设计方法.最后通过仿真实例验证了该控制方法能使具有时延的网络控制系统稳定.     

一种基于区间分割的网络控制系统时延补偿方法

网络控制系统中的时延是影响系统稳定性和动态性能的一个关键因素．本文在对网络时延进行大量测量分析的基础上，通过改进传统网络控制系统结构，提出了基于区间分割的时延补偿方法．仿真结果表明该方法可以显著改善网络控制系统的性能．     

基于动态矩阵的随机时延丢包网络控制系统

针对网络控制系统(NCS)中存在的时延可能大于一个采样周期,以及网络传输存在丢包可能的情况,提出了改进的动态矩阵控制(DMC)算法,通过在线纠正系统的阶跃响应系数来处理时延造成的系统误差,并通过建立缓存器,当数据传输过程中出现丢包时利用动态矩阵算法计算控制量及未来输出预测值的冗余信息来替代丢失的实时信息,减少丢包对系统性能的影响.最后通过基于TrueTime的实时仿真系统研究,对比了不同时延及丢包率情况下改进算法与传统动态矩阵控制算法的效果,表明随着时延和丢包率的增大,改进动态矩阵算法的优势明显,从而验证了该方法的有效性.     

网络控制系统中在线时延测量及时延补偿

为了克服网络控制系统中随机时延对控制性能产生的影响,提出了基于在线时延测量和一步预测输出(基于在线参数辨识)的随机时延补偿方法。通过对以太网中随机时延的分析,提出一种在以太网中不存在同步时钟的情况下在线测量时延的方法;在在线辨识模型参数基础上,得到对象一步(随机时延小于一个采样周期)预测输出,从而根据测得的时延得到对象由于随机时延而引起的输出量变化;再用变化量加上对象输出量用于控制算法反馈。最后通过基于以太网控制实验平台对液位对象进行控制所得的结果,验证了时延测量方法和随机时延补偿算法的有效性。     

基于时延预估的网络控制系统研究

针对基于Internet环境下的控制系统中，网络时延、丢包、包序颠倒等问题对闭环实时控制系统性能造成的影响，提出了一种基于时延预估补偿控制的闭环实时控制方法。该方法引入一个网络时延存储队列，对网络时延进行在线预估，通过Smith预估器对控制系统进行动态补偿。同时，利用一类非线性PID的非线性特性对传统PID控制器进行改进，以改善时延随机性对控制系统的影响。仿真结果证明，该方法对模型精确已知的一类控制对象有较好的控制效果。     

智能控制：从学习控制到平行控制

20世纪60年代,学习控制开启了人类探究复杂系统控制的新途径,基于人工智能技术的智能控制随之兴起.本文以智能控制为主线,阐述其由学习控制向平行控制发展的历程.本文首先介绍学习控制的基本思想,描述了智能机器的架构设计与运行机理.随着信息科技的进步,基于数据的计算智能方法随之出现.对此,本文进一步简述了基于计算智能的学习控制方法,并以自适应动态规划方法为切入点分析非线性动态系统自学习优化问题的求解过程.最后,针对工程复杂性与社会复杂性互相耦合的复杂系统控制问题,阐述了基于平行控制的学习与优化方法求解思路,分析其在求解复杂系统优化控制问题方面的优势.智能控制思想经历了学习控制、计算智能控制到平行控制的演化过程,可以看出平行控制是实现复杂系统知识自动化的有效方法.     

Supervisory control of switching control systems

In this paper, the problem of designing a switching policy for an adaptive switching control system is formulated as a problem of supervisory control of a discrete-event system (DES). Two important problems in switching control are then addressed using the DES formulation and the theory of supervisory control under partial observation. First, it is verified whether for a given set of controllers, a switching policy satisfying a given set of constraints on the transitions among controllers exists. If so, then a minimally restrictive switching policy is designed. Next, an iterative algorithm is introduced for finding a minimal set of controllers for which a switching policy satisfying the switching constraints exists. It is shown that in the supervisory control problem considered in this paper, limitations on event observation are the factors that essentially restrict supervisory control. In other words, once observation limitations are respected, limitations on control will be automatically satisfied. This result is used to simplify the proposed iterative algorithm for finding minimal controller sets.     

From robust control to adaptive control

The paper emphasizes the interaction between robust control, identification in closed loops and adaptive control. Robust control and recent algorithms developed for plant model identification in closed loops have led to new designs of adaptive control systems. Their performances are further enhanced by the use of multiple-model adaptive control, based on switching and tuning. These developments are illustrated by their application to the control of a flexible transmission system.     

Stochastic optimal control of networked control systems with control packet dropouts

This paper considers the stochastic optimal control problem for networked control systems(NCSs)with control packet dropouts.The proportional plus up to the third-order derivative(PD3)compensation strategy is adopted to compensate for control packet dropouts at the actuator by using the past control packets stored in the buffer.Based on the strategy,a new NCS structure model with packet dropouts is provided,where the packet dropout is assumed to obey the Bernoulli random binary distribution.In terms of the given model,the stochastic optimal control law is proposed. Numerical examples illustrate the effectiveness of the results.     

Hyperbolic optimal control and fuzzy control

In this paper, we consider a new approach to fuzzy control which entails the formulation of a novel state-space representation and a new form of optimal control problem. Basically, in this new formulation, linear functions in the conventional state-space representation and cost functional are replaced by hyperbolic functions. We give a solution for this new, infinite-time, optimal control problem, which we call hyperbolic optimal control. Furthermore, we show that the resulting optimal controller is in fact a Mamdani-type fuzzy controller with Gaussian membership functions and center of gravity defuzzification. These results enable us to investigate analytically important issues, such as stability and robustness, pertaining to fuzzy controllers as well as add a powerful theoretical framework to the field of fuzzy control     

Survey on iterative learning control,repetitive control,and run-to-run control

In this paper, three control methods—iterative learning control (ILC), repetitive control (RC), and run-to-run control (R2R)—are studied and compared. Some mathematical transformations allow ILC, RC, and R2R to be described in a uniform framework that highlights their similarities. These methods, which play an important role in controlling repetitive processes and run-based processes, are collectively referred to as learning-type control in this paper. According to the classification adopted in this paper, learning-type control has two classes—direct form and indirect form. The main ideas and designing procedures for these two patterns are introduced, separately. Approximately 400 papers related to learning-type control are categorized. Statistical analysis of the resulting data reveals some promising fields for learning-type control. Finally, a flowchart based on the unique features of the different methods is presented as a guideline for choosing an appropriate learning-type control for different problems.     

Generalized control systems,boundary control systems,and delayed control systems

We investigate the relationships between the three classes of systems mentioned in the title: we show that systems with delays in control are a special instance of boundary control systems, and a boundary control system produces a generalized control system when projected onto its (unstable) eigenspaces. We use this observation to investigate the action of feedback on the dynamical behavior of systems with boundary controls. In particular, the well-known fact that spectral controllability is necessary and sufficient for a system with delays in control to be stabilizable is derived from a general rather than from anad hoc method. This paper was written according to the programs of the GNAFA-CNR group, with the financial support of the Italian “Ministero della Pubblica Istruzione.”     

Embedded model control: reconciling modern control theory and error-based control design

The paper addresses the problem of reconciling the modern control paradigm developed by R. Kalman in the sixties of the past century, and the centenary error based design of the proportional, integrative and derivative (PID) controllers. This is done with the help of the error loop whose stability is proved to be necessary and sufficient for the close loop plant stability. The error loop is built by cascading the uncertain plant to model discrepancies (causal, parametric, initial state, neglected dynamics), which are driven by the design model output and by arbitrary bounded signals, with the control unit transfer functions. The embedded model control takes advantage of the error loop and its equations to design appropriate algorithms of the modern control theory (state predictor, control law, reference generator), which guarantee the error loop stability and performance. A simulated multivariate case study shows modeling and control design steps and the coherence of the predicted and simulated performance.