On the model-based control of networked systems

Montestruque, Luis A.; Antsaklis, Panos J.

doi:10.1016/s0005-1098(03)00186-9

Cited by 515 publications

(283 citation statements)

References 11 publications

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“…Internet-based control, a new type of control systems, is characterized as globally remote monitoring and adjustment of plants over the Internet. In recent years, Internet-based control systems have gained considerable attention in science and engineering [1][2][3][4][5][6], since they provide a new and convenient unified framework for system control and practical applications. Examples include intelligent home environments, windmill and solar power stations, small-scale hydroelectric power stations, and other highly geographically distributed devices, as well as tele-manufacturing, tele-surgery, and tele-control of spacecrafts.…”

Section: Introductionmentioning

confidence: 99%

Passive Robust Control for Internet-Based Time-Delay Switching Systems

Zhang¹,

Yan²

2011

Robust Control, Theory and Applications

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Section: Introductionmentioning

confidence: 99%

Passive Robust Control for Internet-Based Time-Delay Switching Systems

Zhang¹,

Yan²

2011

Robust Control, Theory and Applications

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“…This approach belongs to the class of methods exploiting a deterministic mathematical model of the plant. In this class we also have the well-known model-based approach developed by Montestruque and Antsaklis (2003;. From our point of view the main difference between these two methods is that the first one is well suited for problems requiring efficient compensation of network introduced delays, while the second one is oriented towards increasing the sampling period and, as a consequence, the reduction in the bandwidth required for stable operation of an NCS.…”

Section: Introductionmentioning

confidence: 99%

Approach to the design of robust networked control systems

Morawski¹,

Zajaczkowski²

2010

International Journal of Applied Mathematics and Computer Science

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The paper describes the application of the traffic engineering framework together with application layer procedures as mechanisms for the reduction of network latency lags. These mechanisms allow using standard and inexpensive hardware and software technologies typically applied for office networking as a means of realising networked control systems (NCSs) with high dynamic control plants, where a high dynamic control plant is the one that requires the sampling period several times shorter than communication lags induced by a network. The general discussion is illustrated by experimental results obtained in a laboratory NCS with the magnetic levitation system (MLS), which is an example of a structurally unstable plant of high dynamics.

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“…Networked control systems result from connecting these system components via a communication network such as controller area network (CAN), PROFIBUS or Ethernet. An increasing amount of research addresses the distributed control of inter-connected dynamical processes: stability and control [3][4][5], decision, co-ordination and scheduling [6,7], diagnosis of discrete event systems [8] and fault tolerance [9 -11]. However, only a few studies of the impact of the communication network on the diagnosis of continuous systems have recently been published [12 -14].…”

mentioning

confidence: 99%

Transmission delays in residual computation

Llanos

Staroswiecki

Colomer

et al. 2007

IET Control Theory Appl.

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The design of control, estimation or diagnosis algorithms most often assumes that all available process variables represent the system state at the same instant of time. However, this is never true in current network systems, because of the unknown deterministic or stochastic transmission delays introduced by the communication network. During the diagnosing stage, this will often generate false alarms. Under nominal operation, the different transmission delays associated with the variables that appear in the computation form produce discrepancies of the residuals from zero. A technique aiming at the minimisation of the resulting false alarms rate, that is based on the explicit modelling of communication delays and on their best-case estimation is proposed. IntroductionOwing to the growing complexity and spatial distribution of automated systems, communication networks have become the backbone of most control architecture. As systems are required to be more scalable and flexible, they have additional sensors, actuators and controllers, often referred to as field (intelligent) devices [1,2]. Networked control systems result from connecting these system components via a communication network such as controller area network (CAN), PROFIBUS or Ethernet. An increasing amount of research addresses the distributed control of inter-connected dynamical processes: stability and control [3][4][5], decision, co-ordination and scheduling [6,7], diagnosis of discrete event systems [8] and fault tolerance [9 -11]. However, only a few studies of the impact of the communication network on the diagnosis of continuous systems have recently been published [12 -14].In model-based fault detection and isolation (FDI), a set of residuals that should be ideally zero in the fault-free case and different from zero, in the faulty case are designed [15 -17]. However, in practice, residuals are different from zero, not only because of measurement noise, unknown inputs and modelling uncertainties but also because of transmission delays. Since no network can communicate instantaneously, data which are used in the residual computation do not represent the state of the system at the time of the computation. Instead, they represent the state of the system at some (often unknown) time prior to the computation. Moreover, each variable being possibly transmitted under a different transmission delay, the whole set of data that are used in the residual computation may even not be consistent with the system state at any moment prior to the computation. Therefore, residuals that should theoretically be zero in the non-faulty case might create false alarms as the result of transmission delays.The false alarms rate can be decreased by increasing the decision threshold, at the cost of reducing the sensitivity to faults. In this paper, a technique aiming at the minimisation of the false alarms caused by transmission delays without increasing the number of missed detection is proposed. It relies on the explicit modelling of communication delays, and...

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On the model-based control of networked systems

Cited by 515 publications

References 11 publications

Passive Robust Control for Internet-Based Time-Delay Switching Systems

Passive Robust Control for Internet-Based Time-Delay Switching Systems

Approach to the design of robust networked control systems

Transmission delays in residual computation

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