Implementation of a Control Loop Experiment in a Network-Based Control System With LonWorks Technology and IP Networks

Canovas, S.; Cugnasca, Carlos Eduardo

doi:10.1109/tie.2010.2040562

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…Furthermore, industrial distributed systems and industrial networks (Canovas and Cugnasca [13], Cucinotta et al [14], Felser, Jasperneite, and Gaj [15], Gaj, Jasperneite, and Felser [16]) are pushing the curve with respect to real-time communication needs. Realtime needs for enhanced reality and telepresence (Gelenbe, Hussain, and Kaptan [17]) are also creating specific needs in online QoS management for multimedia systems (Silvestre-Blanes et al [18]).…”

Section: Routing For Real-time Trafficmentioning

confidence: 99%

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

Wang

2017

Prob. Eng. Inf. Sci.

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G-Network queueing network models, and in particular the random neural network (RNN), are useful tools for decision making in complex systems, due to their ability to learn from measurements in real time, and in turn provide real-time decisions regarding resource and task allocation. In particular, the RNN has led to the design of the cognitive packet network (CPN) decision tool for the routing of packets in the Internet, and for task allocation in the Cloud. Thus in this paper, we present recent research on how to dynamically create the means for quality of service (QoS) to end users of the Internet and in the Cloud. The approach is based on adapting the decisions so as to benefit users as the conditions in the Internet and in Cloud servers vary due to changing traffic and workload. We present an overview of the algorithms that were designed based on the RNN, and also detail the experimental results that were obtained in three areas: (i) traffic routing for real-time applications, which have strict QoS constraints; (ii) routing approaches, which operate at the overlay level without affecting the Internet infrastructure; and (iii) the routing of tasks across servers in the Cloud through the Internet.

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Section: Routing For Real-time Trafficmentioning

confidence: 99%

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

Wang

2017

Prob. Eng. Inf. Sci.

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“…Guaranteeing the control performance, that is, quality of performance (QoP), is an important requirement of industrial NCSs. Studies have already discussed the relationship between the quality of service (QoS) in network systems and the QoP in control systems [23], [24]. A low QoS may result in QoP degradation, such as tracking errors.…”

Section: Introductionmentioning

confidence: 99%

Quality of Performance Aware Data Transmission for Energy-Efficient Networked Control

2021

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The energy consumption of network interfaces in networked control systems (NCSs) must be reduced for sustainability. The sleep mechanisms of energy-efficient network interfaces for networked motor control systems, in which controller and motor devices are connected by communication networks, have attracted much interest in recent years. They reduce the energy consumption of network interfaces by making the transmitters installed on the controller and motor devices enter a sleep period when no data are to be transmitted. In a conventional send-on-delta (SoD)-based sleep control method, the network interfaces decide when to enter the sleep period based on the variations of the control input and response values. Although this method effectively reduces the communication rate for reducing energy consumption, it does not explicitly consider the quality of performance (QoP) or control performance such as tracking errors. In high-precision motion control systems, ensuring a certain QoP level is crucial. This study proposes a QoPaware sleep control method for energy-efficient networked motor control systems to maintain the QoP at a certain level while operating the sleep mechanism. Here, the QoP is defined as the tracking error between the command and the response values. In the proposed method, network interfaces decide when to enter the sleep period based on the tracking error. Experimental results confirm that the QoP-aware sleep control method outperforms the SoD-based sleep control method in terms of the tracking errors (i.e., integral square error and steady-state error) at the expense of increased communication rates.INDEX TERMS Data transmission, energy-efficient network, motion control, networked control system, remote control, quality of performance.

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“…Furthermore, industrial distributed systems and industrial networks [13][14][15][16] are pushing the curve with respect to real-time communication needs. Real-time needs for enhanced reality and telepresence [17] are also creating specific needs in on-line QoS management for multimedia systems [18].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic over the Cognitive Packet Network

Wang

Gelenbe

2016

Computer Networks

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Abstract. Real-Time services over IP (RTIP) have been increasingly significant due to the convergence of data networks worldwide around the IP standard, and the popularisation of the Internet. Real-Time applications have has strict Quality of Service (QoS) constraint, which poses a major challenge to IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement, and in this paper we design and experimentally evaluate the"Real-Time (RT) over CPN" protocol which uses QoS goals that match the needs of real-time packet delivery in the presence of other background traffic under varied traffic conditions. The resulting design is evaluated via measurements of packet delay, delay variation (jitter) and packet loss ratio.

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Implementation of a Control Loop Experiment in a Network-Based Control System With LonWorks Technology and IP Networks

Cited by 16 publications

References 8 publications

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

Quality of Performance Aware Data Transmission for Energy-Efficient Networked Control

Real-Time Traffic over the Cognitive Packet Network

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