Robust mixed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" display="inline" overflow="scroll"><mml:msub><mml:mrow><mml:mi mathvariant="script">H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant="script">H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> control of networked control systems with random time delays in both forward and backward communication links

Shi, Yang; Yu, Bo

doi:10.1016/j.automatica.2011.01.022

Cited by 303 publications

(113 citation statements)

References 17 publications

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“…Through calculating the transition probability matrix for the multistep delay mode jump, an output feedback controller was derived and the sufficient and necessary conditions for the stochastic stability were established. Furthermore, the mixed H 2 /H ∞ control problem of NCSs with S-C delays and C-A delays governed by two Markov chains was investigated in [70], where a twomode-dependent output feedback controller was designed based on the available S-C and C-A delay values. In [71], the S-C and C-A delays were also modeled as two Markov chains and a state-feedback controller based on both the plant mode and the delay mode was designed to guarantee the stochastic stability of NCSs.…”

Section: Considering Both S-c Delays and C-a Delaysmentioning

confidence: 99%

Modeling of Random Delays in Networked Control Systems

Chen

Jiang

et al. 2013

Journal of Control Science and Engineering

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In networked control systems (NCSs), the presence of communication networks in control loops causes many imperfections such as random delays, packet losses, multipacket transmission, and packet disordering. In fact, random delays are usually the most important problems and challenges in NCSs because, to some extent, other problems are often caused by random delays. In order to compensate for random delays which may lead to performance degradation and instability of NCSs, it is necessary to establish the mathematical model of random delays before compensation. In this paper, four major delay models are surveyed including constant delay model, mutually independent stochastic delay model, Markov chain model, and hidden Markov model. In each delay model, some promising compensation methods of delays are also addressed.

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Section: Considering Both S-c Delays and C-a Delaysmentioning

confidence: 99%

Modeling of Random Delays in Networked Control Systems

Chen

Jiang

et al. 2013

Journal of Control Science and Engineering

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“…For systems with network problems existing on both the forward and backward channels, Markov chains were employed to model the delays and packet dropouts and, subsequently, incorporated into the well-known two-mode-dependent state feedback controller in the general and practical way [20]. The robust stability of this control method was significantly improved through the implementation of the H2 and H∞ norms to perform the robust H2 and robust mixed H2/H∞ two-mode-dependent controller [21], [22]. Several studies also developed the robust controllers based on the other advanced concepts, in which the network delays are presented in the general form and bounded by both upper and lower limits to reduce the conservatism problem [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Predictive Control System Over Imperfect Networks

Dinh

Ahn

Marco

2017

IEEE Trans. Ind. Electron.

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Abstract-This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1-delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2-a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3-using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4-based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach.Index Terms-Networked control system, time delay, state feedback control, predictor, neural network, buffer. I. INTRODUCTIONETWORKED control systems (NCSs) are spatially distributed systems, in which actuators and sensors at the plant side are connected to a controller at the remote side. Due to having many advantages over the traditional control schemes via point-to-point wiring, NCSs have been deploying worldwide in various applications, such as power grids, transportation systems, teleoperation or remote systems.However for practical uses of NCSs, the most important issues are network-included random time-varying delays and packet dropouts which deteriorate the control performances and, easily cause the instability [1], [5], [25], and [29]. Generally, time delays can be classified into three components: computation delays at the controller and communication delays at the forward and backward channels.Many studies in literature addressed stability of NCSs with delay problem only [1]- [6]. Here, the controller designs were depended on the assumptions in which the time delay was constant [1], bounded [2]- [5] or had a probability distribution function [6]. Other researchers focused on analyzing time delay problem at communication channels to evaluate its effects on the system performances as well as to compensate these undesirable effects [7] and [8]. For NCSs with large delays, a new control concept based on variable sampling periods using neural network or prediction theories has been recently adopted [9]- [11]. Nevertheless, the observation of real delay data to train and construct the NCSs was not appropriately discussed in these studies. To solve this problem, an advanced variable sampling period control concept has been developed for systems containing random delays [29]. The effectiveness of this concept in accurately detecting and predicting the delays without requiring a training process was proved through real-time ...

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“…The present work contemplates a Markov chain-driven NCS [21], [10], [33], [38], [39], which is an special case of stochastic NCS [40], [25], [11]. Driving the NCS by a finite state Markov chain enables to consider not only states with different delay distributions (the stochastic case) but also transition probabilities among states.…”

Section: Introductionmentioning

confidence: 99%

A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System

Cuenca

Ojha²,

Salt

et al. 2015

Information Sciences

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ElsevierCuenca Lacruz, ÁM.; Ojha, U.; Salt Llobregat, JJ.; Chow, M. (2015). AbstractIn this work, a non-uniform multi-rate control strategy is applied to a kind of Networked Control System (NCS) where a wireless path tracking control for an Unmanned Ground Vehicle (UGV) is carried out. The main aims of the proposed strategy are to face time-varying network-induced delays and to avoid packet disorder. A Markov chain-driven NCS scenario will be considered, where different network load situations, and consequently, different probability density functions for the network delay are assumed. In order to assure mean-square stability for the considered NCS, a decay-rate based sufficient condition is enunciated in terms of probabilistic Linear Matrix Inequalities (LMIs). Simulation results show better control performance, and more accurate path tracking, for the scheduled (delay-dependent) controller than for the non-scheduled one (i.e. the nominal controller when delays appear). Finally, the control strategy is validated on an experimental test-bed.

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Robust mixed H2/H∞ control of networked control systems with random time delays in both forward and backward communication links

Cited by 303 publications

References 17 publications

Modeling of Random Delays in Networked Control Systems

Modeling of Random Delays in Networked Control Systems

A Novel Robust Predictive Control System Over Imperfect Networks

A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System

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