Network-based control systems: A tutorial

被引:142
|
作者
Chow, MY [1 ]
Tipsuwan, Y [1 ]
机构
[1] N Carolina State Univ, Advance Diag & Control Lab, Dept Elect & Comp Engn, Raleigh, NC 27606 USA
关键词
delay; time constraint; network-based control; networked control;
D O I
10.1109/IECON.2001.975529
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
For many years now, data networking technologies have been widely applied in the control of industrial and military applications. These applications include manufacturing plants, automobiles, and aircrafts. Connecting the control system components in these applications, such as sensors, controllers, and actuators, via a network can effectively reduce the complexity of the systems with nominal economical investments. Furthermore, the applications connected through a network can be remotely controlled from a long-distance source. Traditionally, the networks used in the aforementioned applications are specific industrial networks, such as CAN (Controller Area Networks), and PROFIBUS. However, general data networks such as Ethernet and Internet are rapidly advancing to be the networks of choices for many applications due to their flexibility and lower costs. There are two general structures to design a control system through a network. The first structure is to have several subsystems, in which each of the subsystem contains a set of sensors, a set of actuators, and a controller by itself. These system components are attached to the same control plant. In this case, a subsystem controller receives a set point from the central controller. Another structure is to connect a set of sensors and a set of actuators to a network directly. Sensors and actuators in this case are attached to a plant, while a controller is separated from the plant via a network connection to perform a closed-loop control over the network. Both structures have different advantages. The first structure is more modular. A control loop is simpler to be reconfigured. The second structure has better interaction because data are transmitted to components directly. A controller in the second structure can observe and process every measurement, whereas a (central) controller in the first structure may have to wait until the set point is satisfied to transfer the complete measurements, status signals, or alarm signals. A control system in the second structure is so-called networked control system or network-based control system depending on different authors' preference. A challenging problem in control of networked-based system is network delay effects. The time to read a sensor measurement and to send a control signal to an actuator through the network depends on network characteristics such as their topologies, routing schemes, etc. Therefore, the overall performance of a network-based control system can be significantly affected by network delays. The severity of the delay problem is aggravated when data loss occurs during a transmission. Moreover, the delays do not only degrade the performance of a network-based control system, but also can destabilize the system. This tutorial presents fundamental details of network-based control and recent network-based control techniques for handling the network delays. The techniques are based on various concepts such as state augmentation, queuing and probability theory, nonlinear control and perturbation theory, and scheduling. A general structure of a network-based control system, delay types, and delay behaviors are also described in this tutorial. In addition, advantages and disadvantages of these techniques are discussed.
引用
收藏
页码:1593 / 1602
页数:4
相关论文
共 50 条
  • [21] Neural network-based virtual sensors in flight control systems
    Tranchero, B
    Latorre, C
    AUTOMATIC CONTROL IN AEROSPACE 2001, 2002, : 375 - 380
  • [22] Analysis and Design of Network-Based Control Systems with Binary Modulation
    Zhao, Shunli
    Yin, Xunhe
    Wei, Xueye
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [23] Network-based H∞ feedback control for uncertain stochastic systems
    Wu, Junli
    Karimi, Hamid Reza
    Shi, Peng
    INFORMATION SCIENCES, 2013, 232 : 397 - 410
  • [24] Network-based robust exponential fuzzy control for uncertain systems
    Saravanakumar, Ramasamy
    Hoon Joo, Young
    MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 2022,
  • [25] Robust exponential stabilization for network-based switched control systems
    Dan Ma
    Jian-Chang Liu
    International Journal of Control, Automation and Systems, 2010, 8 : 67 - 72
  • [26] Neural network-based robust control for uncertain systems with nonlinearity
    Luan, Xiaoli
    Liu, Fei
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES A-MATHEMATICAL ANALYSIS, 2006, 13 : 411 - 413
  • [27] Stability analysis of communication network-based process control systems
    Kolla, SR
    EMERGING TECHNOLOGIES UPDATE, VOL II, 2002, 426 : 131 - 136
  • [28] State feedback control of network-based systems with packet disordering
    Jinna Li1
    2. Key Lab of Industrial Informatics
    3. Institute of Systems Science
    Journal of Systems Engineering and Electronics, 2011, 22 (02) : 306 - 313
  • [29] A neural network-based approach to hybrid systems identification for control
    Fabiani, Filippo
    Stellato, Bartolomeo
    Masti, Daniele
    Goulart, Paul J.
    Automatica, 2025, 174
  • [30] Neural network-based H∞ tracking control for robotic systems
    Chang, YC
    IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS, 2000, 147 (03): : 303 - 311