Using AQM for performance improvement of networked control systems

Azadegan, Masoumeh; Beheshti, Mohammad Taghi Hamidi; Tavassoli, Babak

doi:10.1007/s12555-014-0012-9

Cited by 15 publications

(17 citation statements)

References 36 publications

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“…The linearization of the above model around an operating point ( w 0 , q 0 , p 0 ) yields

({,, \begin{array}{c} center & δ \overset{w}{true} ((),, t) = \frac{- N}{τ_{0}^{2} c} ((),, italicδw ((),, t) + italicδw ((),, t - τ ((),, t))) \\ center & - \frac{1}{τ_{0}^{2} c} ((),, italicδq ((),, t) + italicδq ((),, t - τ ((),, t))) \\ center & - \frac{τ_{0}^{2} c}{2 N^{2}} italicδp ((),, t - τ ((),, t)) \\ center & δ \overset{q}{true} ((),, t) = \frac{N}{τ_{0}} italicδw ((),, t) - \frac{1}{τ_{0}} italicδq ((),, t) \end{array})

in which δw = w − w 0 , δq = q − q 0 and operating point is

w_{0} = \frac{τ_{0}}{N}, p_{0} = \frac{2}{w_{0}^{2}}

and

τ_{0} = \frac{q_{0}}{C} + T_{p}

, which is obtained by setting

\overset{w}{true} ((),, t) = 0, \overset{q}{true} ((),, t) = 0

. Therefore, the round trip delay τ ( t ) can be written as the sum of a fixed delay and a time varying delay, which depends on the queue length as below.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

“…Solid lines in Fig. denote the state responses with initial values

x_{0} = {[\begin{matrix} center \\ center 1 & center 1 \end{matrix}]}^{T}

using the control algorithm proposed in . By applying theorem 2 and using the LMI Toolbox of the MATLAB, the controller gains are obtained as follows:

\begin{matrix} left \\ W = 10^{3} * [\begin{matrix} center \\ center 1.8257 & center - 0.2803 & center 0 & center 0 \\ center - 0.2803 & center 0.2862 & center 0 & center 0 \\ center 0 & center 0 & center 2.0836 & center - 0.0614 \\ center 0 & center 0 & center - 0.0614 & center 0.0411 \end{matrix}] \\ \overset{true˜}{K} = [\begin{matrix} center \\ center Y_{1} & center 0 \\ center 0 & center Y_{2} \end{matrix}] W^{- 1} = [\begin{matrix} center \\ center \begin{array}{c} center & - 1.5858 \\ center & 0 \end{array} & center \begin{array}{c} center & - 8.03 \\ center & 0 \end{array} & center \begin{array}{c} center & 0 \\ center & 0.0002 \end{array} & center \begin{array}{c} center & 0 \\ center & 0.0034 \end{array} \end{matrix}] \\ \Rightarrow K_{normalp} = [\begin{matrix} center \\ center - 1.5858 & center - 8.03 \end{matrix}], {normalK}_{normaln} = [\begin{matrix} center \\ center 0.0002 & center 0.0034 \end{matrix}] \end{matrix}

…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…In , a pair of communication sequences that preserve observability was first identified and then an observer‐based feedback controller based on those sequences was designed. In , a co‐design on the control system and the communication network parameters was performed to take into account the interaction between the control and communication systems for the first time. The proposed approach makes it possible to solve communication problems and control problems simultanously, thus contributing to a more efficient NCS design.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed approach makes it possible to solve communication problems and control problems simultanously, thus contributing to a more efficient NCS design. The approach proposed in was used in to design a guaranteed cost control for transmission control protocol (TCP) and NCS.…”

Section: Introductionmentioning

confidence: 99%

“…In and , only the controller to actuator delay has been considered, while in this paper both sensor to controller and controller to actuator delays will be considered to achieve accurate modeling and consequently improve the performance of the NCS. Based on this modeling, augmenting the control system and the TCP communication models results in multiple delay systems for the overall model of NCS.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust Stability and Stabilization Of TCP‐Networked Control Systems with Multiple Delay System Modeling

Azadegan

Beheshti

2017

Asian Journal of Control

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This paper presents a new model for networked control systems (NCSs) under transmission control protocol (TCP) as a multiple‐delay system by considering both sensor to controller and controller to actuator delays. An analytical TCP model has been considered for the network part, and an active queue management (AQM) controller is designed to regulate the desired queue length, which ensures holding the network induced delay and its variation within their lower bounds. The model is assumed to possess structured uncertainties due to the stochastic nature of the network. Robust stability and stabilization conditions are derived in terms of linear matrix inequalities (LMIs) by applying the Lyapunov‐Krasovskii stability criterion. Illustrative examples are presented and it has been shown that the proposed method will obtain less conservative results compared to the existing approaches in the literature.

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“…The linearization of the above model around an operating point ( w 0 , q 0 , p 0 ) yields

({,, \begin{array}{c} center & δ \overset{w}{true} ((),, t) = \frac{- N}{τ_{0}^{2} c} ((),, italicδw ((),, t) + italicδw ((),, t - τ ((),, t))) \\ center & - \frac{1}{τ_{0}^{2} c} ((),, italicδq ((),, t) + italicδq ((),, t - τ ((),, t))) \\ center & - \frac{τ_{0}^{2} c}{2 N^{2}} italicδp ((),, t - τ ((),, t)) \\ center & δ \overset{q}{true} ((),, t) = \frac{N}{τ_{0}} italicδw ((),, t) - \frac{1}{τ_{0}} italicδq ((),, t) \end{array})

in which δw = w − w 0 , δq = q − q 0 and operating point is

w_{0} = \frac{τ_{0}}{N}, p_{0} = \frac{2}{w_{0}^{2}}

and

τ_{0} = \frac{q_{0}}{C} + T_{p}

, which is obtained by setting

\overset{w}{true} ((),, t) = 0, \overset{q}{true} ((),, t) = 0

. Therefore, the round trip delay τ ( t ) can be written as the sum of a fixed delay and a time varying delay, which depends on the queue length as below.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

“…Solid lines in Fig. denote the state responses with initial values

x_{0} = {[\begin{matrix} center \\ center 1 & center 1 \end{matrix}]}^{T}

using the control algorithm proposed in . By applying theorem 2 and using the LMI Toolbox of the MATLAB, the controller gains are obtained as follows: