变采样网络控制系统的鲁棒控制

对于线性时不变控制对象,在控制器和控制对象都采用时间-事件驱动时系统就变成便采样网络控制系统,当网络时延不确定时,在小于或者等于一个变采样周期时,基于动态输出反馈对变采样网络控制系统进行建模,使用李雅普诺夫方法和线性矩阵不等式研究了系统的鲁棒稳定性,并设计了鲁棒控制器,最后给出实例证明在鲁棒控制器的控制下系统稳定。     

不确定时延输出反馈网络化系统保性能控制

研究了一类具有不确定时延的动态输出反馈网络控制系统的保性能控制问题．针对小于等于一个采样周期的不确定时延,利用Lyapunov理论和线性矩阵不等式方法,推导出了动态输出反馈网络控制系统保性能控制律存在的条件,给出了动态输出反馈保性能控制律设计方法．利用LMI工具箱中的目标函数最小化工具,可以求得该类系统优化的保性能控制律参数和优化的性能指标,从而获得该系统的保性能控制律．给出的仿真算例说明了设计方法是有效的所得结果是可行的．     

Lurie网络化控制系统的保性能控制

以不确定Lurie系统作为被控对象,研究其在网络环境下的保性能控制问题.在同时考虑随机网络诱导时延和数据丢包的情况下,建立不确定Lurie网络化控制系统模型;利用Lyapunov方法分别给出了存在结构不确定性和范数有界的不确定性时,Lurie网络化控制系统保性能控制器的设计方法.所得结果是以线性矩阵不等式的形式给出的,便于数值求解.最后以数值实例说明了所提出方法的可行性和有效性.     

不确定长时延网络控制系统的H∞鲁棒控制

在状态不完全可测的网络控制系统中,针对不确定网络长时延、网络噪声等问题系统性能造成的影响,研究了一类具有不确定长时延网络控制系统的H∞鲁棒控制问题.在不确定网络时延大于一个采样周期时,将采用动态输出反馈的网络控制系统建模为一类具有参数不确定性的线性离散系统.基于Lyapunov稳定性理论和线性矩阵不等式(LMI)方法,...     

长时延和丢包的网络控制系统保性能控制

为了研究具有长时延和丢包的网络控制系统保性能控制问题,假设传感器由时钟驱动,控制器和执行器由事件驱动,网络时延在两个采样周期之间,丢包具有马尔科夫跳变特性,利用状态增广的方法,把具有长时延和丢包的网络控制系统建模为具有两种运行模式的离散时间马尔可夫跳变系统,把网络时延转化为系统参数矩阵的不确定性.在此基础上,利用马尔可夫线性跳变系统理论和线性矩阵不等式方法,推导出使网络控制系统随机稳定的保性能控制律存在的充分条件,并给出了设计方法.数例仿真说明了所用方法的有效性.     

变采样网络控制系统的最优保性能控制

针对具有时变有界采样周期和时延的网络控制系统,首先将其建模成一类离散动态区间系统.在此基础上,对一个给定的二次型性能指标,研究了系统的保性能控制问题,基于线性矩阵不等式(LMI)给出最优保性能控制器设计方法.利用LMI工具箱,求解十分方便.仿真结果表明该方法的有效性.     

Lurie网络化控制系统的保性能控制

以不确定Lurie系统作为被控对象,研究其在网络环境下的保性能控制问题.在同时考虑随机网络诱导时延和数据丢包的情况下,建立不确定Lurie网络化控制系统模型,利用Lyapuniv方法分别给出了存在结构不确定性和范数有界的不确定性时,Lurie网络化控制系统保性能控制器的设计方法.所得结果是以线性矩阵不等式的形式给出的,便于数值求解.最后以数值实例说明了所提出方法的可行性和有效性.

     

一类不确定时延网络控制系统最优H∞控制

研究了一类不确定时延网络控制系统最优H∞控制问题针对传感器时钟驱动、控制器和执行器事件驱动的网络控制系统，考虑不大于一个采样周期的不确定时延和有限能量的外部扰动，利用李亚普诺夫理论和线性矩阵不等式描述方法，推导出该系统动态输出反馈H∞控制律存在的充要条件，给出了最优动态输出反馈H∞控制律设计方法通过Matlab数值仿真算例，证明了分析方法和结果的有效性     

移动机器人网络控制系统的输出反馈控制

以线性时不变系统为被控对象,建立了四轮移动机器人网络控制系统的离散数学模型。诱导时延是影响系统性能的关键因素,通过在节点中设置缓冲区的方法可以将网络控制系统中的随机诱导时延转化为确定性时延,从而将网络控制系统由随机系统转化为确定性系统。通过被控对象移动机器人控制实验系统,设计了一个能处理网络诱导时延的输出反馈控制器,分析了采样周期和网络诱导时延对网络控制系统稳定性的影响。仿真结果表明了该控制器和控制策略的正确性及有效性。     

不确定时延网络控制系统的保性能控制

在工程实际网络控制系统中,不仅要求系统能够保持稳定,还要系统具有一定的性能指标,采用状态反馈控制是一种非常有效的控制方式。针对一类线性不确定时延网络控制系统,研究了在有记忆状态反馈控制器下的保性能控制问题。利用Lyapunov函数和线性矩阵不等式的方法,给出了网络控制系统保性能控制律存在的条件和相应的状态反馈控制器的设计方法。最后,通过Matlab数值仿真算例验证了该方法的有效性和正确性。     

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.     

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.     

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.     

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.