An improved event-triggered communication mechanism and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.gif" overflow="scroll"><mml:msub><mml:mi mathvariant="script">L</mml:mi><mml:mi>∞</mml:mi></mml:msub></mml:math>control co-design for network control systems

Li, Fuqiang; Fu, Jingqi; Du, Dajun

doi:10.1016/j.ins.2016.01.073

Cited by 56 publications

(20 citation statements)

References 32 publications

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“…In order to illustrate the effectiveness of the proposed approach, a satellite control system in is used, and the uncertain parameters are taken into consideration in this example. The model can be described as the system with

\begin{matrix} A_{0} = ([], \begin{matrix} array 0 & array 1 & array 0 & array 0 \\ array - 0.09 & array - 0.0219 & array 0.09 & array 0.0219 \\ array 0 & array 0 & array 0 & array 1 \\ array 0.09 & array 0.0219 & array - 0.09 & array - 0.029 \end{matrix}), \\ B_{0} = ([], \begin{matrix} array 0 \\ array 0 \\ array 0 \\ array 1 \end{matrix}), G = ([], \begin{matrix} array 0 & array 0 & array 0 & array 0 \\ array - 0.13 & array - 0.1 & array 0.13 & array 0.1 \\ array 0 & array 0 & array 0 & array 0 \\ array 0.13 & array 0.1 & array - 0.13 & array - 0.1 \end{matrix}), \\ F false(t false) = ([], \begin{matrix} array \sin (t) & array 0 & array 0 & array 0 \\ array 0 & array \cos (t) & array 0 & array 0 \\ array 0 & array 0 & array \sin (t) & array 0 \\ array 0 & array 0 & array 0 & array \cos (t) \end{matrix}), \\ E_{a} = ([], \begin{matrix} array 0.1 & array 0 & array 0 & array 0 \\ array 0 & array 0.02 & array 0 & array 0 \\ array 0 & array 0 & array 0.1 & array 0 \\ array 0 & array 0 & array 0 & array 0.02 \end{matrix}), E_{b} = ([]) \end{matrix}

…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Thus, can be rewritten into .

\overset{V}{true} false(t false) + δ V ((), t) \leq γ ε^{τ_{M}} ε^{- α t} + ϕ .

Then applying comparison lemma to , we can obtain

V false(t false) \leq e^{- δ t} V false(0 false) + \int_{0}^{t} e^{- δ false(t - s false)} ((), \overset{γ}{true} ε^{- α s} + ϕ) d s,

where

\overset{γ}{true} = γ ε^{τ_{M}}

. Obviously, it is that

ε^{- α s} = e^{- ((), α \ln ε) s}

, so we can derive from that

V false(t false) \leq e^{- δ t} ((), V false(0 false) - ϕ false/ δ) + ϕ false/ δ + \overset{γ}{true} e^{- δ t} \int_{0}^{t} e^{((), δ - α \ln ε) s} d s .

Next, according to the values of γ , ε , α and ϕ , two cases are considered to : If

δ - α \ln ε = 0

, we have

V false(t false) \leq e^{- δ t} ((), V false(0 false) - ϕ false/ δ + \overset{γ}{true} t) + ϕ false/ δ .

δ - α \ln ε \neq 0

, we obtain

\begin{matrix} right V (t \end{matrix}

…”

Section: Appendixmentioning

confidence: 99%

“…For instance, in , the authors set the event‐triggered threshold to be the sum of a proportional threshold times the quadratic form of the transmitted signals and an additional constant. In , the condition of the event‐triggered threshold was set the same as in , but the constant was replaced by an exponential decay function. Recently, Li et al.…”

Section: Introductionmentioning

confidence: 99%

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Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Liu

2018

Asian Journal of Control

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This paper is concerned with modeling and controlling under the mixed event‐triggered mechanism (ETM) for networked control systems (NCSs) with time‐varying delays and uncertainties. Firstly, an event‐triggered threshold is set by using both state and state‐independent informations in the mixed ETM. Then the event‐triggered NCSs with network‐induced time‐varying delays which exist in both sensor‐to‐controller and controller‐to‐actuator channels are modeled as a general time‐delay system. Based on the piecewise differentiable characteristic of the time‐varying delay and by using the approach of free weighting matrix and reciprocally convex, a less conservative criterion to be globally uniformly ultimately bounded (GUUB) stability and a controller design method are derived. Furthermore, an algorithm is proposed to obtain the desired mixed ETM and state‐feedback controller which can render the network load and control performance to reach an expected level. Compared with the relative and absolute ETMs, the proposed mechanism can effectively improve the transmission efficiency during the whole working time. Finally, a numerical example is given to show the effectiveness of the proposed approach.

show abstract

\begin{matrix} A_{0} = ([], \begin{matrix} array 0 & array 1 & array 0 & array 0 \\ array - 0.09 & array - 0.0219 & array 0.09 & array 0.0219 \\ array 0 & array 0 & array 0 & array 1 \\ array 0.09 & array 0.0219 & array - 0.09 & array - 0.029 \end{matrix}), \\ B_{0} = ([], \begin{matrix} array 0 \\ array 0 \\ array 0 \\ array 1 \end{matrix}), G = ([], \begin{matrix} array 0 & array 0 & array 0 & array 0 \\ array - 0.13 & array - 0.1 & array 0.13 & array 0.1 \\ array 0 & array 0 & array 0 & array 0 \\ array 0.13 & array 0.1 & array - 0.13 & array - 0.1 \end{matrix}), \\ F false(t false) = ([], \begin{matrix} array \sin (t) & array 0 & array 0 & array 0 \\ array 0 & array \cos (t) & array 0 & array 0 \\ array 0 & array 0 & array \sin (t) & array 0 \\ array 0 & array 0 & array 0 & array \cos (t) \end{matrix}), \\ E_{a} = ([], \begin{matrix} array 0.1 & array 0 & array 0 & array 0 \\ array 0 & array 0.02 & array 0 & array 0 \\ array 0 & array 0 & array 0.1 & array 0 \\ array 0 & array 0 & array 0 & array 0.02 \end{matrix}), E_{b} = ([]) \end{matrix}

…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Thus, can be rewritten into .

\overset{V}{true} false(t false) + δ V ((), t) \leq γ ε^{τ_{M}} ε^{- α t} + ϕ .

Then applying comparison lemma to , we can obtain

V false(t false) \leq e^{- δ t} V false(0 false) + \int_{0}^{t} e^{- δ false(t - s false)} ((), \overset{γ}{true} ε^{- α s} + ϕ) d s,

where

\overset{γ}{true} = γ ε^{τ_{M}}

. Obviously, it is that

ε^{- α s} = e^{- ((), α \ln ε) s}

, so we can derive from that

V false(t false) \leq e^{- δ t} ((), V false(0 false) - ϕ false/ δ) + ϕ false/ δ + \overset{γ}{true} e^{- δ t} \int_{0}^{t} e^{((), δ - α \ln ε) s} d s .

Next, according to the values of γ , ε , α and ϕ , two cases are considered to : If

δ - α \ln ε = 0

, we have

V false(t false) \leq e^{- δ t} ((), V false(0 false) - ϕ false/ δ + \overset{γ}{true} t) + ϕ false/ δ .

δ - α \ln ε \neq 0

, we obtain

\begin{matrix} right V (t \end{matrix}

…”

Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Liu

2018

Asian Journal of Control

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“…Compared to [6,15,16,25], we synthesize continuous event-triggered controllers while these works are all dedicated to the case of discrete event-triggered control, i.e., the plant dynamics is first discretized and then an eventtriggered controller is designed for the discrete-time system, which is a different sampling paradigm. Moreover, we consider different types of exogenous inputs affecting the control system, as the plant is subject to external disturbances and both the output measurement and the control input are corrupted by noise.…”

Section: Introductionmentioning

confidence: 99%

Co-design of output feedback laws and event-triggering conditions for theL2-stabilization of linear systems

et al. 2018

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. Co-design of output feedback laws and event-triggering conditions for the L2 -stabilization of linear systems. Automatica, Elsevier, 2018, 87, pp.337-344. 10.1016/j.automatica.2017 Co-design of output feedback laws and event-triggering conditions for the L 2 -stabilization of linear systems AbstractWe investigate the L2-stabilization of linear systems using output feedback event-triggered controllers. In particular, we are interested in the scenario where the plant output and the control input are transmitted to the controller and to the actuators, respectively, over two different digital channels, which have their own sampling rule. The plant dynamics is affected by external disturbances and the output measurement and the control input are corrupted by noises. We present a co-design procedure to simultaneously synthesize dynamic output feedback laws and event-triggering conditions such that the closed-loop system is L2-stable with a given upper-bound on the L2-gain. The required conditions are formulated in terms of the feasibility of linear matrix inequalities (LMIs). Then, we exploit these LMIs to maximize the guaranteed minimum time between two transmissions of the plant output and/or of the control input. We also present a heuristic method to reduce the amount of transmissions for each channel. The developed technique encompasses time-driven (and so periodic) sampling as a particular case and the result is also new in this context. The effectiveness of the proposed methods is illustrated on a numerical example.

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“…Compared with a time‐triggered control scheme, the event‐triggered control strategy has more advantages in terms of saving network resources and prolonging the lifetime of microprocessors. In recent years, a lot of results on consensus and output tracking based on an event‐triggered mechanism for MASs have appeared . A novel consensus protocol based on a decentralized event‐triggered mechanism and another based on a distributed event‐triggered control scheme were proposed to reach output consensus and average agreement, respectively.…”

Section: Introductionmentioning

confidence: 99%

Cooperative output regulation of heterogeneous multiagent systems based on event‐triggered control with fixed and switching topologies

Jia

Zhao

2017

Intl J Robust & Nonlinear

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Summary This paper addresses the cooperative output regulation problem of multiagent systems with fixed and switching topologies. Each agent is a heterogeneous linear system, and the output of the exosystem can be available to only a subset of agents. For the agents that can directly access the exosystem, a common observer based on an event‐triggered strategy is constructed to estimate the exogenous signal for feedback control design. For the rest of the agents, estimators based on an event‐triggered mechanism to acquire the estimation value of the exogenous signal are designed under some essential assumptions. A decentralized event‐triggered formulation is considered first by applying a Lyapunov function for a fixed topology. Furthermore, a topology‐dependent triggering condition and the average dwell‐time switching law are deduced simultaneously by using multiple Lyapunov functions for switching topologies. Under communication constraints, we propose observer‐based and estimator‐based feedback controllers to solve the cooperative output regulation problem using available local information among agents. Two examples are finally provided to verify the effectiveness of the proposed theoretical results.

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An improved event-triggered communication mechanism andL∞control co-design for network control systems

Cited by 56 publications

References 32 publications

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Co-design of output feedback laws and event-triggering conditions for theL2-stabilization of linear systems

Cooperative output regulation of heterogeneous multiagent systems based on event‐triggered control with fixed and switching topologies

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