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462 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 1, FEBRUARY 2013

An Integrated Design Framework of Fault-TolerantWireless Networked Control Systems for Industrial

Automatic Control ApplicationsSteven X. Ding, Ping Zhang, Shen Yin, and Eve L. Ding

Abstract—In this paper, a design framework of fault-tolerantwireless networked control systems (NCSs) is developed for indus-trial automation applications. The main objective is to achieve anintegrated parameterization and design of the communication pro-tocols, the control and fault diagnosis algorithms aiming atmeetinghigh real-time requirements in industrial applications. To illustratethe design framework, a laboratory wireless fault-tolerant NCSplatform is presented.

Index Terms—Fault detection, fault-tolerant systems, networkedcontrol systems, time-varying systems, wireless networks.

I. INTRODUCTION

T HE rapid development of microelectronic, informa-tion, and communication technologies has significantly

enhanced networking of sensors, actuators, controllers, andmicroprocessors and accelerated the application of networkedcontrol systems (NCSs) in major industrial sectors. This trendis strongly driven by the industrial needs for distributed processcontrol and networked embedded systems [1]–[3]. In recentyears, research on NCSs receives considerably enhanced at-tention in the control community [4]–[7]. The main focusesof the research activities are on system performance analysis[8] and controller design [9]–[11] under consideration ofnetwork-induced effects, which are expressed in terms of theso-called quality of service (QoS) parameters. The major QoSparameters of a network are data transmission delays, jitter,packet loss, and network failure rates. Investigations on thecodesign of the network QoS and the controller parametersbuild the mainstream in this research field [3], [5]–[7].Application of NCSs to automatic control of distributed pro-

cesses is the state of the art in major industrial sectors. SuchNCSs are distributed and consist of: 1) a great number of PNC(process near component) nodes with integrated sensors, actua-

Manuscript received August 07, 2011; revised December 07, 2011; acceptedAugust 07, 2012. Date of publication August 21, 2012; date of current versionDecember 19, 2012. Paper no. TII-11-403.S. X. Ding is with the Institute for Automatic Control and Complex Sys-

tems, University of Duisburg-Essen, 47057 Duisburg, Germany (e-mail: [email protected]).P. Zhang is with the Rhine-Waal University of Applied Sciences, Germany.S. Yin is with the Institute of Intelligent Control and Systems, Harbin Institute

of Technology, 150001 Harbin, China.E. L. Ding is with the Department of Physical Engineering, University of

Applied Sciences, 45877 Gelsenkirchen, Germany.Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TII.2012.2214390

tors and microprocessors; 2) control stations (CSs), each ofwhich coordinates and supervises a set of PNC nodes; and 3)communication systems that network the CSs at the higher leveland the PNC nodes at the lower level.Studies in the past have revealed that data transmission de-

lays, jitter, and packet loss rate strongly depend on the net-work load [12]. In particular, for those networks like Ethernet,WLAN, the randomness in the QoS parameters may increaserapidly as the network is heavily loaded. The main reason forit is the use of the carrier sense multiple access with collisiondetection (CSMA/CD) approach as the medium access control(MAC) protocol. In a typical industrial NCS, the number of thenodes and the networked sensors, actuators as well as micropro-cessors is constant during the normal process operation and onlyvaries in case of faults. On the other hand, to meet high real-timecontrol performance and reliability requirements, the QoS pa-rameters should satisfy the requirements of the highest Classof Service. These facts motivate the automation industry to de-velop new network solutions to reduce the random behavior indata transmissions and to enhance the real-time ability of thenetwork.Both in the application and research domains, wireless com-

munications are viewed as a future-oriented trend in automaticcontrol [13], [14]. In the research area, much attention has beenpaid to performance analysis of wireless NCSs (W-NCSs) [15]and development of new control and observer schemes [16],[17]. The major efforts in industrial automation are devoted tothe development of standards and protocols and new wirelessnetworks [18]–[20].A most critical issue surrounding the design of NCSs is to

meet the requirements on system reliability and dependability,while guaranteeing a high system performance [21]. In recentyears, a number of methods have been developed for achievingreliable and robust fault detection and identification (FDI) aswell as fault-tolerant control (FTC) in NCSs [7], [22]–[24].Analog to the trends in the control and filtering of NCSs, themajor research focus is on dealing with uncertainties in NCSscaused by data transmission delays and packet losses.It is worth noticing that the research activities in the con-

trol community are mainly dedicated to the development of ad-vanced control and FDI schemes with the integrated QoS pa-rameters, while the automation industry fully concentrates ondeveloping new generation of networks and MAC mechanisms[12], [25]. For a wide application of the W-NCSs in industry,an advanced design framework is needed, which allows an in-tegrated design of controllers, FDI units and networks. In this

1551-3203/$31.00 © 2012 IEEE

DING et al.: INTEGRATED DESIGN FRAMEWORK OF FAULT-TOLERANT WIRELESS NCSs FOR INDUSTRIAL AUTOMATIC CONTROL APPLICATIONS 463

Fig. 1. Fault-tolerant W-NCS configuration.

way, not only the required control and FDI performance but alsohigh real-time ability and reliability can be achieved. This is themotivation of our work. The main objective of this paper is todevelop a framework, based onwhich aW-NCS can be designedand constructed for the real-time application in industrial au-tomation. The core of this framework is an integrated design oftheMAC protocols, the control and FDI schemes, which allows:1) a deterministic data transmission via wireless networks; 2) areduced data transmission amount and at the same time; and 3)meeting the requirements on the control and FDI performance.To illustrate our study, WiNC, an experimentation platform forfault-tolerant wireless networked control, will be presented.

II. OUTLINE OF THE FAULT-TOLERANT W-NCS DESIGNFRAMEWORK AND PROBLEM FORMULATION

Here, the process and control system models are first de-scribed. The basic ideas behind the integrated design frameworkof FTW-NCSs are then highlighted. Finally, the major topics tobe addressed are formulated.

A. Process and Control Loop Models

Suppose that the process under consideration consists ofsub-processes modeled by

(1)

where , denotes the state vector of the thsubprocess, and , , and are known matrices of appro-

priate dimensions. feedback control loops are applied for theregulation of the th subprocess with actuators

...

(2)

and associated with it (3)

and sensors, ,

... (4)

where , denote the actuators/controllers and sensors em-bedded in the th control loop of the th subprocess. It is as-sumed that the sensors are nominally modeled by

(5)

with known matrix . In the following, the th subprocessand the associated actuators and sensors embedded inthe control loops are called the th subsystem. For the real-timeimplementation, the maximum allowed sampling time dependson the process dynamics. We denote the critical sampling timefor the th subprocess by .

B. Outline of the System Configuration

In order to achieve high reliability and to meet the demandsfor the control performance, theW-NCS configuration sketchedin Fig. 1 is proposed, which consists of the following.• Execution layer: At this layer, PNC nodes are integratedinto (local) feedback control loops.

• Coordination and supervision layer: This layer consists ofCSs. They coordinate and synchronize the the overall


W-NCS operation. Another task of the CSs is processmonitoring, in which observer-based FDI algorithms areimplemented.

• Management layer: In our study, FTC is implemented inthe context of resource management [26]. Any component,either sensor or actuator or process component, is definedas system resource associated with some functionality. Afault in one component is considered as a loss of the corre-sponding resource or redundancy and activates a resourcereallocation.

C. Communication Structure

As shown in Fig. 1, the data transmissions in the NCS can beclassified as: 1) communications within a subsystem, which willoperate in a master–slave mode with the CS as the master and 2)communications between the CSs, which serve as synchroniza-tion and execution of the control, monitoring, and communica-tion actions, and, in case of a fault, the activation and executionof the resource reallocation and FTC algorithms. The data ex-changes at this layer are periodic and regulated by a protocole.g., in the token passing mechanism. In the industrial real-timecontrol systems, data transmissions are often regulated basedon the simplified ISO/OSI three-layer model [12], in which thephysical layer is standardized. At the data link layer, also calledMAC, and at the application layer, the user is able to imple-ment a scheduler that guarantees the required real-time perfor-mance and regulates the QoS parameters of the network. In ourstudy, scheduler design will be done for the both communica-tion forms.

D. Problem Formulation

The basic idea of establishing a framework for the design ofa fault-tolerant W-NCS is to address the multilayer fault-tol-erant control and communication systems in an integrated way.In order to meet the real-time requirements and ensure a de-terministic data transmission, TDMA mechanism is adopted inthe proposed framework. In this way, the MAC protocol can bemodeled in form of a scheduler, whose design and parameteri-zation will be achieved in a codesign with the development ofthe control and FDI algorithms at the different functional layers.For our purpose, the control and FDI schemes to be developedshould: 1) enable the realization of standard control and FDIschemes; 2) be easily parameterized; and 3) take into accountthe distributed process structure. In addition, to reduce the datatransmission costs, the scheduler parameters for the data trans-missions as well as the synchronization within and between theCSs should be integrated into the development of the controllersand FDI/FTC algorithms.

III. INTEGRATED DESIGN FRAMEWORK FOR AN FT W-NCS

Here, we describe the integrated design framework for an in-dustrial fault-tolerant W-NCS shown in Fig. 1.

A. Integrated Design at the Execution Layer

We first present the local control and FDI structures, algo-rithms and the corresponding scheduling mechanism for thedata transmissions within a subsystem.

1) On the Data Transmissions: Following the TDMAmech-anism, the data transmissions within a subsystem are periodic.Let be the maximum data transmission time (including phys-ical transmission and software operation times) between anytwo nodes within the th subsystem. Define a time slot .An operating cycle includes: i) time slots for the transmis-sion of sensor data; 2) time slots for control commands; and3) time slots for implementation of the communicationstrategy. Let be the cyclic time with

(6)

To ensure the required deterministic real-time behavior,should be bounded by the critical sampling time , i.e.,

(7)

where defines the upper bound of the maximum data trans-mission time between any two nodes within the th subsystemand is a parameter for dimensioning the capacity of the thsubnet.Different from the application of robust control and filtering

theory as, for instance, reported in [27]–[29], in the integrateddesign framework data transmission delays and packet loss aremainly dealt with: 1) by dimensioning the capacity of the com-munication nets according to (7) and 2) by running special com-munication actions during the time slots reserved for the com-munication. Supported by the TDMAmechanism, the first mea-sure ensures that the influence of the transmission delays can begenerally neglected. In case that the transmission delay is largerthan , it will be treated as a missing packet. The handling ofpacket loss is realized by means of the communication actionslike ACK, RTS (for repeating sending).2) On Local Control and FDI Algorithms: Assume that an

estimate of , denoted by , is available at the be-ginning of the th cycle, and sampling of is done at thetime instant where is someinteger. The following (local) control law is proposed:

(8)

(9)

where is the reference signal and received, to-gether with from the th CS, denotes a stablediscrete-time LTI system which serves as the parameter transfermatrix, , . The first term in (8) isan observer-based state feedback law, while the second term isthe feedback of , which builds the so-called residualsignal vector. For the sake of simplifying the notation and designissues it is assumed that andthus . It is wellknown that residual generation is the first step for a successfulFDI. Based on , , , faultsin the sensors and actuators embedded in the control loopcan be detected and isolated. The reader is referred to [31] forthe existing algorithms.3) Realization of the Data Transmissions and the Scheduler:

Note that in the proposed W-NCS configuration ,


instead of the sensor data, will be transmitted to the th CS,immediately after it is generated. Considering that the valuerange of is generally smaller than the one of

and, moreover, the residual signals contain allinformation needed for the controllers and observers, trans-mitting results in reduced transmission costswithout losing needed information. It follows from (8) thatall controllers , share receivedfrom the th CS. It motivates saving the data ,

, , in one packet. In the multicastway, this packet is sent by the th CS to all control loopsin the th subsystem. Having received the data from the thCS, each control loop will decode the packet for ,

. As a result, it holds for in (6) ,, then .

B. Integrated Design at the Supervision and CoordinationLayer

Recall that the th CS will receive the residual signals andsend the state estimate and the reference signals to the associatedcontrol loops in each cycle time. While the reference signals

are set by the user, will be deliveredby an observer. For the observer and controller design purpose,the subprocess model (1) is discretized with the sampling time

to yield

(10)

In order to construct an observer for estimating , informationabout the couplings with the other CSs expressed in terms of

, , is needed. Suppose that in the time interval

updates in the th CS are realized at the timeinstants

, and assume for

for . It turns out

(11)

where denotes the last time instant, at which a up-date in the -th CS is realized before the time instant :

no update

in the

update

in theno update in the

...update in

the

As a result, the following discrete-time model is obtained forthe th subsystem:

(12)

with , . Note

that is time-varying.For the purpose of constructing the observers at the CSs,

the following scheduler for the wireless networking of the CSsis proposed. Assume that , , can be expressedby with an integer . Let , be

(13)

In the time interval , the th CS,, will, in the broadcast mode, send the estimate for

to all other CSs at the time instants. It is evident that this

scheduler is periodic with as period time.Remember that the subsystems may have different cycle

times. The introduction of the scheduler for the communica-tions among the CSs is helpful to synchronize the actions in thesubsystems. For this purpose, we introduce the definition: thetime instants, , aredenoted by , , and they are ordered as

.


Based on the above scheduler and the discrete subsystemmodel (12), the observer embedded in the th CS is constructedas follows:

... (14)

where is the observer gain matrix to be designed.Since the scheduler results in a periodic data transmis-sions, it holds .For the implementation of (14), the th CS receives: 1)

, sent by the (local) control loopsand 2) , sent by the other CSs, during the timeinterval . The residual signal is usedboth for improving the estimation performance and, togetherwith the available , , ,for the computation of the local controller

while is applied for constructing , as definedin (11), which describes the couplings between the th CS andthe other CSs.It is well known that a successful FDI for the components

embedded in the th subsystem can be achieved: 1) by a suitableselection of the stable post-filter for building

(15)

and 2) by evaluating and threshold settings [31]. It is worthnoting that the th CS has access to all available residual signalsfrom the control loops within the subsystem. As a result, the FDIbased on is more reliable and efficient in comparison witha local FDI algorithm.

C. Management Layer

Depending on application purposes, the management layercan be designed individually. In the laboratory WiNC platformthat will be described in Section V, a resource monitoring isbuilt, which is driven by fault knowledge provided by the FDIalgorithms. It is realized in form of a database, in which theavailable sensors (including observers as soft sensors), actu-ators, communication systems, process components togetherwith their redundancy are clustered in terms of their role forexecuting a defined functionality (e.g., control or FDI). Re-source management and reallocation can be formulated as anoptimization problem and solved by means of an optimizationalgorithm [26]. In this paper, the management layer designissues will not be addressed.

TABLE IMAIN DESIGN PARAMETERS

IV. DESIGN ISSUES

It follows from (7), (14), and (15) that the control, FDI al-gorithms, and communications are well parameterized, as sum-marized in Table I. This section deals with the design issues ofthese controller, observer and FDI parameters.

A. Overall Model of the W-NCS

To begin with, the overall W-NCS is modeled based on thediscrete-timemodels of the subsystems and the scheduler for thecommunications between the subsystems, which are periodicwith the period time . To this end, ,

, in the time interval

(16)

are first brought into a vector. To simplify the notation, ,as defined in (16) will be applied in the sequel if no con-

fusion is caused. We denote this vector by and, withoutloss of the generality, suppose that

...

...

...

...(17)

where denotes the vector consisting of all of those statevariables that have a update at the time instant . Again, forthe sake of simple notation, is now introduced to denotethe set of all of those subsystems, which have a update at thetime instant . It is clear that the vector works like abuffer with a variable length, in which all state variables in thetime interval are saved. At the next time instant

, the state variables at the time instant are removedfrom the buffer and those at the time instant are added. Asa result, is formed, which includes all state variables


in the time interval . This procedure can bemodeled by

(18)

...

...

(19)

After a straightforward computation, we have finally

...

...

......

......

...

...

(20)

In this way, , , and can be determined. It isclear that (18) is a linear time-periodic (LTP) system.

B. Observer Design

Using (20), the observer algorithm (14) can be rewritten as

...

.... . .

. . .

(21)

where , , respectively, denote the estimationfor , , the residual vector

generated in the th subsystem with , and in-cludes all the residual vectors generated in the subsystems be-longing to . Recall that

...

It turns out

...

...

(22)

Now, taking into account the special form of the overall NCSmodel (18), we can rewrite the observers of the form (14)into one (distributed) observer as follows:

(23)

(24)

(25)

whose error dynamics is governed by

(26)

(27)

In [32], observer design schemes for LTP systems are presented,which can be applied for our purpose.

C. Controller Design

To begin with, the idea behind the control law (8) is briefly in-troduced. Let be the plant modelof a feedback control loop. It has been proven in [30] that all sta-bilizing controllers (the so-called Youla parameterization [33])can be written as

(28)


where is a state feedback gain matrix, is a stable param-eterization matrix, is an estimation delivered by

(29)

and . Comparing (8) and (28) makes itclear that (8) is a local realization of (28) as far as isdelivered by a full order observer like (29). For the purpose ofdesigning the controllers given in (8), apply the control law (8)to (18), which leads to

(30)

. . .

. . .

. . .

... (31)

with being the time instant equal to the one of the th rowblock in the state vector , numbering like

...

...

...

...

Hence, the overall system dynamics is described by

(32)

(33)

The basic requirement on the selection of is formulated asfinding so that the system

is stable. To this end, the approaches, e.g., given in [32] forLTP controller design can be applied. Recall that the secondterm in the control law (8) is the feedback of the residual vector

. The selection of a stable post-filter

. . .

will have no influence on the system stability, and can be dedi-cated to improving the system robustness.

D. FDI Algorithms

Both at the execution and supervision and coordinationlayers, the core of the FDI algorithms consists of the computa-tion of the post-filters. For our purpose, the design of ,

, , is based on the local model

(34)

(35)

where the terms , model the unknown inputs e.g.,caused by the couplings with other subsystems, process, andsensor noises, etc. Similarly, the design of the post-filter ,

, is based on

(36)

(37)

Note that both models, (34)–(35) and (36)–(37), are LTP sys-tems. In [34] and [35], a decoupling approach and a unified so-lution for the LTP systems are presented, which can be used forthe design of the local and higher level FDI subsystems.

E. Advanced Design Issues

Below, some advanced design issues for improving theW-NCS performance, robustness, and reliability are addressed.1) Increasing Data Transmissions Among the Subsystems:

Recall that the observer (14) at the th subsystem is driven onlyby the (local) residual vector . As a result, the struc-ture of the observer gain matrix is restricted. One way to im-prove the observer performance is to increase the data transmis-sions between the sub-systems. To this end, the -th sub-systemcan transmit together with in one packetto all of the other subsystems.2) Enhancing System Robustness and Reliability: In the pre-

vious study, uncertainties caused by for instance a linearization,sampling errors and packet drops have not been taken into ac-count by modeling. In order to increase the robustness of LTPsystems against disturbances and unknown inputs, the designschemes presented in [32] and [35] can be applied. Recently,there are a great of number of publications addressing the packetdrop issue in NCSs for the control issues [9], [28], [36], for thefiltering and observer design issues [37], [38], as well as for theFDI issues [29], [39]–[41].


Fig. 2. Schematic description of the WiNC platform.

V. EXPERIMENTATION PLATFORM WINC

Parallel to the theoretical study, efforts have been made toconstruct a W-NCS platform, called WiNC [26]. This work isstrongly motivated by the need to demonstrate the applicationof the proposed design framework. To this end, standard wire-less cards supporting IEEE 802.11a/b/g standards have beenselected. In almost every available 802.11 hardware the MACprotocol employs the CSMA/CA approach. In order to achievea reliable deterministic data transmissions, a new MAC pro-tocol optimized for industrial wireless communication using theavailable 802.11 based hardware is developed in our lab usingSoftMAC [42]. In SoftMAC several measures are taken to pro-vide real-time capability. The result is that the carrier sensingand the backoff procedure can be controlled and no additionalpackets, like ACK or RTS/CTS, occur. SoftMAC allows user-defined protocol implementation.

A. Platform Configuration

Fig. 2 shows the configuration of the WiNC platform that iscomposed of several functionalities, a driver, and a protocolstack optimized for industrial wireless communication. In theplatform, the communication functionality of organizing thenetwork access by use of a scheduler is based on time slotsand tailored. The control, observer and FDI functionalities areintegrated into the WiNC application layer. The protocol stackintegrates a simplified addressing scheme with a custom packetstructure and robust channel coding techniques, thus ensuringdeterministic transmission times. The main technical data are:1) hardware: desktop computers with x86 architecture micro-processors, equipped with one D-Link DWL-AG530 Tri-ModeDual-band PCI card and one data acquisition card and 2)

software: Linux operating system (Fedora 6 with RT-PreemptPatch 2.6.20-rt8), SoftMAC, and COMEDI (COntrol andMEasurement Device Interface).

B. Interfaces for the Parameterization and NCS Monitoring

WiNC is supported by a graphic user interface (GUI) and anNCS monitor. The GUI is essential for the realization of theintegrated design, and consists of: 1) a parameter setting forthe controllers, observers, and FDI algorithms and 2) a directaccess to the MAC and setting of the scheduler. By means ofthis GUI, the control and FDI parameters given in Table I, andthe scheduler described in the last sections can be directly givenby the user. It is also possible to input these parameters in theMATLAB/SIMULINK environment. The WiNC monitor includes:1) the standard functions like plots of all control and outputvariables; 2) the diagnostic module that displays all fault typesand plots of residual signals; and 3) system statistics includingthe control performance, the QoS of the network.

C. Tests and Applications

The WiNC has been tested under different working condi-tions. For this purpose, a test bed has been built with two well-known laboratory setups: Three-Tank as sub-system 1 with acritical sampling time larger than 1 s and Inverted Pendulumas sub-system 2 with a critical sampling time 50 ms. The firstsubsystem is composed of three level sensors and two pumpsas actuators. The second one has two sensors and one actuator.One of the tests was to run the system over night so that the wire-less transmission channels remain relatively time-invariant. Inthis test, a fixed antenna setup has been used with all sensorsand actuators being attached to the plants in the laboratory andthe controller being placed one floor below. The 2.4 GHz band


and the binary phase shift keying (BPSK) modulation with thedirect sequence spread spectrum (DSSS) spreading techniquewhich is expected to provide the most reliable communicationof all available modulation methods of the 802.11a/b/g stan-dards have been applied. Data have been collected for two sce-narios: without flexible retransmission request (FRR) and withFRR. For these scenarios, the transmission power of all cardshas been varied in the range from 1 to 16 dBm. In [26], someof the test results have been reported in details. WiNC and thebenchmark setups have also been successfully used in researchand education projects. In [26] and [39] successful realizationsof the FDI schemes for the subsystems Inverted Pendulum andThree-Tank are reported.

VI. CONCLUSION

In this paper, a design framework for a fault-tolerant W-NCShas been presented for industrial automatic control with highreal-time requirements. The essential idea of this framework isthe integrated design and parametrization of the control, FDIalgorithms and communication nets in a multilayer system con-figuration. The core of the W-NCS is the application of a dis-tributed observer and the local residual generators that serveboth for the control, FDI, and communication purposes. Thedesign and construction of the observer and residual generatorsare realized with respect to the TDMA-based scheduler for thedata transmissions. This guarantees a deterministic data trans-mission and allows a system design in the LTP system-theoret-ical framework. In order to demonstrate the proposed integrateddesign framework, a platform, WiNC, has been developed andbriefly presented in this paper.

ACKNOWLEDGMENT

The platformWiNC has been developed by the control (AKS)and communication (NTS) groups at the University of Duis-burg-Essen, which is in part funded by the German ResearchFoundation. The authors would like to thank the NTS groupheaded by Prof. Czylwik for the successful collaboration, Dr.Chihaia, and Mr. Goldschmidt for the technical contributions.Also, the authors are grateful to the anonymous reviewers fortheir valuable comments.

REFERENCES

[1] J. R. Moyne and D. M. Tilbury, “The emergence of industrial controlnetworks formatufacturing control, diagnostics, and safety data,”Proc.IEEE, vol. 95, no. 1, pp. 29–47, Jan. 2007.

[2] J. Baillieul and P. Antsaklis, “Control and communication challengesin networked rea-time systems,” Proc. IEEE, vol. 95, no. 1, pp. 9–28,Jan. 2007.

[3] R. Gupta and M.-Y. Chow, “Networked control system: Overviewand research trends,” IEEE Trans. Ind. Electron., vol. 57, no. 7, pp.2527–2535, Jul. 2010.

[4] H. Ishii and B. Francis, Limited Data Rate in Control Systems WithNetworks. Berlin, Germany: Springer, 2002.

[5] J. P. Hespanha, P. Naghshtabrizi, and Y. Xu, “A survey of recent resultsin networked control systems,” Proc. IEEE, vol. 95, no. 1, pp. 138–162,Jan. 2007.

[6] F.-Y. Wang and D. Liu, Networked Control Systems. Berlin, Ger-many: Springer-Verlag, 2008.

[7] Y. Xia, M. Fu, and G.-P. Liu, Analysis and Synthesis of NetworkedControl Systems. Berlin, Germany: Springer-Verlag, 2011.

[8] W. Zhang,M. Branicky, and S. Phillips, “Stability of networked controlsystems,” IEEE Control Syst., vol. 21, no. 1, pp. 84–99, Feb. 2001.

[9] G.-P. Liu, Y. Xia, D. Rees, and W. Hu, “Networked predictive controlof systems with random network delays in both forward and feedbackchannels,” IEEE Trans. Ind. Electron., vol. 54, pp. 1282–1297, 2007.

[10] C. Huang, Y. Bai, and Y. Liu, “H-infinity state feedback control fora class of networked cascade control systems with uncertain delay,”IEEE Trans. Ind. Inf., vol. 6, no. 1, pp. 62–72, Feb. 2010.

[11] X. Luan, P. Shi, and F. Liu, “Stabilization of networked control systemswith random delays,” IEEE Trans. Ind. Electron., vol. 57, no. 9, pp.4323–4330, Sep. 2010.

[12] F. J. Furrer, Industrieautomation Mit Ethernet-TCP/IP und Web-Tech-nologie. : Hüthig Verlag, 2003.

[13] F. D. Pellegrini, D. Miorandi, S. Vitturi, and A. Zanella, “On the use ofwireless networks at low level of factory automation systems,” IEEETrans. Ind. Informat., vol. 2, no. 2, pp. 129–143, May 2006.

[14] A.Willig, “Recent and emerging topics in wireless industrial communi-cations: A selection,” IEEE Trans. Ind. Inf., vol. 4, no. 2, pp. 102–124,May 2008.

[15] N. J. Ploplys, P. A. Kawka, and A. G. Alleyne, “Closed-loop controlover wireless networks,” IEEE Control Syst., vol. 24, no. 3, pp. 58–71,Jun. 2004.

[16] A. Ulusoy, O. Gurbuz, and A. Onat, “Wireless model-based predictivenetworked control systems over cooperative wireless network,” IEEETrans. Indus. Inf., vol. 7, no. 1, pp. 41–51, Feb. 2011.

[17] J. Chen, X. Cao, P. Cheng, Y. Xiao, and Y. Sun, “Distributed collabora-tive control for industrial automation with wireless sensor and actuatornetworks,” IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4219–4230,Dec. 2010.

[18] ISA-100.11a-2009 Wireless Systems for Industrial Automation:Process Control and Related Applications ISA, 2009, ISA.

[19] A. Willig, “Redundancy concepts to increase transmission reliabilityin wireless industrial LANs,” IEEE Trans. Ind. Inf., vol. 1, no. 3, pp.173–182, Aug. 2005.

[20] M. Jonsson and K. Kunert, “Towards reliable wireless industrial com-munication with real-time guarantees,” IEEE Trans. Ind. Inf., vol. 5,no. 4, pp. 429–442, Nov. 2009.

[21] R. J. Patton, C. Kamphampati, A. Casavola, P. Zhang, S. Ding, and D.Sauter, “A generic strategy for fault-tolerance in control systems dis-tributed over a network,” Eur. J. Control, vol. 13, pp. 280–296, 2007.

[22] H. Fang, H. Ye, and M. Zhong, “Fault diagnosis of networked controlsystems,” Annu. Rev. Control, vol. 31, pp. 55–68, 2007.

[23] C. Aubrun, D. Sauter, and J. Yame, “Fault diagnosis of networked con-trol systems,” Int. J. Appl. Math. Comput. Sci., vol. 18, pp. 525–537,2008.

[24] Z. Mao, B. Jiang, and P. Shi, “Observer-based fault-tolerant control fora class of networked control systems with transfer delays,” J. FranklinInst., vol. 348, pp. 763–776, 2011.

[25] K. Lee, S. Lee, and M. Lee, “QoS-based remote control of networkedcontrol systems via profibus token passing protocol,” IEEE Trans. Ind.Inf., vol. 1, no. 3, pp. 183–191, Aug. 2005.

[26] C. I. Chihaia, “Active fault-tolerance in wireless networked controlsystems,” Ph.D. dissertation, Dept. Electr. Eng., University of Duis-burg-Essen, Duisburg, Germany, 2010.

[27] J. Xiong and J. Lam, “Stabilization of linear systems over networkswith bounded packet loss,” Automatica, vol. 43, pp. 80–87, 2007.

[28] H. Gao, T. Chen, and J. Lam, “A new delay system approach to net-worked-based control,” Automatica, vol. 44, pp. 39–52, 2008.

[29] X. He, Z. Wang, and D. Zhou, “Robust fault detection for networkedsystemswith communication delay and datamissing,”Automatica, vol.45, pp. 2634–2639, 2009.

[30] S. X. Ding, G. Yang, P. Zhang, E. Ding, T. Jeinsch, N. Weinhold, andM. Schulalbers, “Feedback control structures, embedded residual sig-nals and feedcak control schemes with an integrated residual access,”IEEE Trans. Control Syst. Technol., vol. 18, pp. 352–367, 2010.

[31] S. X. Ding, Model-Based Fault Diagnosis Techniques—DesignSchemes, Algorithms, and Tools. Berlin, Germany: Springer-Verlag,2008.

[32] S. Bittanti and P. Colaneri, Periodic Systems, Filtering and Control.Berlin, Germany: Springer, 2009.

[33] K. Zhou, Essential of Robust Control. Englewood Cliffs, NJ: Pren-tice-Hall, 1998.


[34] P. Zhang and S. Ding, “Disturbance decoupling in fault detection oflinear periodic systems,” Automatica, vol. 43, pp. 1410–1417, 2007.

[35] P. Zhang and S. X. Ding, “On fault detection in linear discrete-time,periodic, and sampled-data systems (survey),” J. Control Sci. Eng., pp.1–18, 2008.

[36] H. Gao, X. Meng, T. Chen, and J. Lam, “Stabilization of networkedcontrol systems with dynamic output-feedback controllers,” SIAM J.Contr. Optim., vol. 48, pp. 3643–3658, 2010.

[37] L. Shi, L. Xie, and R. Murray, “Kalman filtering over a packet-de-laying network: A probabilistic approach,” Automatica, vol. 45, pp.2134–2140, 2009.

[38] N. Xiao, L. Xie, and M. Fu, “Kalman filtering over unreliable commu-nication networks with bounded Markovian packet dropouts,” Int. J.Robust Nonlinear Contr., vol. 19, pp. 1770–1786, 2009.

[39] Y. Wang, H. Ye, S. X. Ding, G. Wang, and D. Zhou, “Residual gener-ation and evaluation of networked control systems subject to randompacket dropout,” Automatica, vol. 45, pp. 2427–2434, 2009.

[40] Z. Mao, B. Jiang, and P. Shi, “Protocol and fault detection design fornonlinear networked control systems,” IEEE Trans. Circuits Syst. II,Exp. Briefs, vol. 56, no. 2, pp. 255–259, Feb. 2009.

[41] D. Sauter, S. Li, and C. Aubrun, “Robust fault diagnosis of networkedcontrol systems,” Int. J. Adaptive Contr. Signal Process., vol. 23, pp.722–736, 2009.

[42] M.Neufeld, J. Fifiled, C. Doerr, A. Sheth, andD. Grunwald, “SoftMACflexible wireless research platform,” HotNets, vol. IV, 2005.

Steven X. Ding received the Ph.D. degree in elec-trical engineering from the Gerhard-Mercator Uni-versity of Duisburg, Duisburg, Germany, in 1992.From 1992 to 1994, he was an R&D Engineer with

Rheinmetall GmbH. From 1995 to 2001, he was aProfessor of control engineering with the Universityof Applied Science Lausitz, Senftenberg, Germany,and served as Vice President of this university during1998 to 2000. Since 2001, he has been a Professorof control engineering and the head of the Institutefor Automatic Control and Complex Systems (AKS)

at the University of Duisburg-Essen, Duisburg, Germany. His research interestsare model-based and data-driven fault diagnosis, fault-tolerant systems and theirapplication in different industrial sectors.

Ping Zhang received the Ph.D. degree in control en-gineering from Tsinghua University, Beijing, China,in 2002.From 2002 to 2007, she was with the Institute for

Automatic Control and Complex Systems (AKS),University of Duisburg-Essen, Duisburg, Germany.From 2007 to 2012, she was with the CompetenceCenter of Automation Technology, BASF SE,Germany. Since 2012, she has been a Professorof automation engineering with the Rhine-WaalUniversity of Applied Sciences, Germany. Her

research interests are model and data based fault diagnosis, fault tolerantcontrol, periodic and time-varying systems, networked control systems, plantasset management, and their applications in the process industry, automobileindustry and energy systems.

Shen Yin received the B.E. degree in automationfrom Harbin Institute of Technology, Harbin, China,in 2004, and the M.Sc. degree in control and in-formation system and Ph.D. degree in electricalengineering and information technology from Uni-versity of Duisburg-Essen, Duisburg, Germany, in2007 and 2012, respectively.He is currently with the Institute of Intelli-

gent Control and Systems, Harbin Institute ofTechnology, Harbin, China. His research interestsare model-based and data-driven fault diagnosis,

fault-tolerant control, and their application to large-scale industrial processes.

Eve L. Ding received the Ph.D. degree in mechanicalengineering from the Gerhard-Mercator University,Duisburg, Germany, in 1991.Between 1991–1994, she held academic positions

with the University of Bremen and the Universityof Rostock. From 1995 to 1999, she was an R&DEngineer with Continental Teves AG & Co. OHG,Germany, where she was responsible for the develop-ment of fault diagnosis systems in ESP. Since 1999,she has been a Professor of control engineering withthe University of Applied Science, Gelsenkirchen,

Germany. Her research interests are model-based fault diagnosis, fault-tolerantsystems, and their application in industry with a focus on automotive systems.