DistributedH∞Consensus Control of Networked Control Systems with Time-Triggered Protocol

The analysis and design of traditional networked control systems focused on single closed-loop scenario. This paper introduces a distributed control approach for the networked control systems (NCSs) with multiple subsystems based on a time-triggered network protocol. Firstly, some basic ideas of the time-triggered protocol are introduced and a time schedule scheme is employed for the NCS. Then, a novel model is proposed to the NCS regarding the network-induced delay. The resulting closed-loop system is time-delay linear system considering a distributed control law. A sufficient condition toH∞consensus control is present based on the Lyapunov-Krasovskii function. Also, the controller design approach towards the givenH∞performance index is given by a cone complement linearization and iterative algorithm. Finally, numerical examples are given to validate the approach.

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Finite-time dissipative control for networked control systems with hybrid-triggered scheme

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220946509 ◽

2020 ◽

pp. 014233122094650

Author(s):

Qian Zhang ◽

Huaicheng Yan ◽

Shiming Chen ◽

Xisheng Zhan ◽

Xiaowei Jiang

Keyword(s):

Control Systems ◽

Finite Time ◽

Closed Loop ◽

Controller Design ◽

Networked Control Systems ◽

Sufficient Conditions ◽

Networked Control ◽

Closed Loop System ◽

Network Resources ◽

Dissipative Control

This paper is concerned with the problem of finite-time dissipative control for networked control systems by hybrid triggered scheme. In order to save network resources, a hybrid triggered scheme is proposed, which consists of time-triggered scheme and event-triggered scheme simultaneously. Firstly, sufficient conditions are derived to guarantee that the closed-loop system is finite-time bounded (FTBD) and [Formula: see text] dissipative. Secondly, the corresponding controller design approach is presented based on the derived conditions. Finally, a numerical example is presented to show the effectiveness of the proposed approach.

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Stabilization of Networked Control Systems with Induced Delays and Actuator Saturation

Mathematical Problems in Engineering ◽

10.1155/2016/4208712 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Luo Zhang ◽

Mou Chen ◽

Qingxian Wu ◽

Bei Wu

Keyword(s):

Control Systems ◽

Controller Design ◽

Actuator Saturation ◽

Networked Control Systems ◽

Input Saturation ◽

Feedback Stabilization ◽

Networked Control ◽

Linear Matrix ◽

Control Approach ◽

Weighting Matrix

The problem of state feedback stabilization is studied for networked control systems (NCSs) subject to actuator saturation and network-induced delays. To facilitate the controller design, the NCSs are modeled as a class of discrete-time systems with bounded delays and input saturation. Based on Lyapunov-Krasovskii theory and free weighting matrix approach, the sufficient condition is derived in terms of linear matrix inequality for the asymptotic stability. Finally, the effectiveness of the developed control approach is proved through numerical examples.

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Analysis and Design of Networked Control Systems with Random Markovian Delays and Uncertain Transition Probabilities

Abstract and Applied Analysis ◽

10.1155/2014/486978 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Li Qiu ◽

Chengxiang Liu ◽

Fengqi Yao ◽

Gang Xu

Keyword(s):

Control Systems ◽

Discrete Time ◽

Closed Loop ◽

Networked Control Systems ◽

Transition Probabilities ◽

Lyapunov Stability Theory ◽

Loop System ◽

Networked Control ◽

Closed Loop System ◽

Analysis And Design

This paper focuses on the stability issue of discrete-time networked control systems with random Markovian delays and uncertain transition probabilities, wherein the random time delays exist in the sensor-to-controller and controller-to-actuator. The resulting closed-loop system is modeled as a discrete-time Markovian delays system governed by two Markov chains. Using Lyapunov stability theory, a result is established on the Markovian structure and ensured that the closed-loop system is stochastically stable. A simulation example illustrates the validity and feasibility of the results.

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