Modeling Internet Delay Dynamics Using System Identification

Kamrani, Ehsan; Mehraban, Maryam

doi:10.1109/icit.2006.372220

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…If the NCS is connected with a cyclic service network (IEEE 802.4, IEEE 802.5, PROFIBUS), then the delays are deterministic and easy to incorporate into the system model; however, if the NCS is built on a random access network (Ethernet, CAN), then the delays are caused by the package collisions and are of a stochastic nature, which naturally makes them more difficult to model and to take into account. Methods used to model the delays have ranged from conventional system identification tools [5], to using Markov chains [6], T-S fuzzy models [7], or ARMA models [8]. The compensation of the effects of this unwanted phenomenon has been done with many different approaches, such as: solving the problem as a LQG (Linear Quadratic Gaussian) problem [9]; modeling the delays as disturbances and posing the problem as a robust control problem, and thus trying to solve it with the respective existent tools [10]; introducing queues so as to represent the NCS as a time-invariant system [11]; using a state observer and a delay predictor and compensator, which relies on highly accurate modeling [12]; design of an event-based controller, which is therefore totally independent of the time delays [13].…”

Section: Networked Control Systemsmentioning

confidence: 99%

Dealing With the Effects of Random Time Delay and Data Dropouts in Networked Control Systems Through Robust Control

Nadzinski

Stankovski²,

Gočev³

2016

JEEIT

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Section: Networked Control Systemsmentioning

confidence: 99%

Dealing With the Effects of Random Time Delay and Data Dropouts in Networked Control Systems Through Robust Control

Nadzinski

Stankovski²,

Gočev³

2016

JEEIT

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“…They present a model for such systems and discuss the systems stability. Kamrani and Mehraban [12] propose a novel approach for modeling the end-to-end time-delay dynamics of the Internet using system identification and apply it to control real-time telerobotic operations via Internet with specific Quality of Service (QoS) offerings. Hespanha et al [13] focus on the advances made from 2003 to 2007, such as the approach of modeling NCSs by delayed differential equations (DDEs) and the switched systems approach.…”

Section: Time Delaysmentioning

confidence: 99%

An Overview of Networked Control of Complex Dynamic Systems

Yan

Zhang

et al. 2014

Mathematical Problems in Engineering

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Networked control systems (NCSs) are spatially distributed systems for which communication between sensors, actuators, and controllers is supported by a shared communication network. In recent years, NCSs have brought many innovative impacts to control systems. However, grate challenges are also met due to the network-induced imperfection. In this paper, we particularly discuss various typical networked induced issues; namely, time delays, packet losses, disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization as well as event-triggered data transmission strategy are surveyed; at the same time, some research topics are also discussed. The common goal of discussion on these topics is to reveal the effect of the communication network on the operation of the networked systems.

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“…In order to overcome this limitation, a general and reliable model of the internet delay dynamics and a stable control system platform to manipulate teleoperator via this communication link are required. It is very important to have a deeply understand the end-to-end internet delay dynamics as it directly affects on the quality of service (QoS) [23,24] in real-time applications. It also enable design and develop an efficient teleoperation control system for both real-time and offline applications.…”

Section: Description Of Internet Based On Its Qos Parametersmentioning

confidence: 99%

“…We choose 20,20,1   for the UDP and UDP + TCP cases to minimize the AIC (Akaike's Information Theoretic Criterion) [23][24][25]. The AIC is defined as:…”

Section:  mentioning

confidence: 99%

Real-Time Internet-Based Teleoperation

Kamrani¹

2012

ICA

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Internet-based teleoperation employs robots and internet a two breakthrough technologies to manipulate robots from distance for different applications. Variable and unknown time delay dynamics of internet is the main obstacle for realtime teleoperation via internet. In this paper the internet delay dynamics and its characteristics have been studied based on the measurement in different nodes. Then a black-box model for end-to-end internet delay dynamics has been developed using system identification and Auto-Regressive eXogeneous (ARX) model. Our experimental studies show a regular periodic behaviour in long-term intervals of internet delay variation and also confirm the accuracy and reliability of our theoretical and modelling derivations. This paper also introduces a novel multivariable control method for real-time telerobotic operations via Internet. Random communications delay of the Internet can cause instability in realtime closed-loop telerobotic systems. When a single identification model is used, it will have to adapt itself to the operating condition before an appropriate control mechanism can be applied. Slow adaptation may result in a large transient error. As an alternative, we propose to use a Multiple Model framework. The control strategy is to determine the best model for the current operating condition and activate the corresponding controller. We propose the use of Multi-Model Adaptive Control Theory and Multivariable Wave prediction method to capture the concurrency and complexity of Internet-based teleoperation. The results confirm the efficiency of the proposed technique in dealing with constant and variable delay dynamics of internet.

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Modeling Internet Delay Dynamics Using System Identification

Cited by 12 publications

References 18 publications

Dealing With the Effects of Random Time Delay and Data Dropouts in Networked Control Systems Through Robust Control

Dealing With the Effects of Random Time Delay and Data Dropouts in Networked Control Systems Through Robust Control

An Overview of Networked Control of Complex Dynamic Systems

Real-Time Internet-Based Teleoperation

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