Survey on Recent Advances in Networked Control ... - IEEE Xplore

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2015.2506545, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

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Survey on Recent Advances in Networked Control Systems Xian-Ming Zhang, Qing-Long Han, Senior Member, IEEE, and Xinghuo Yu, Fellow, IEEE

Abstract—Networked control systems are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators and so on). Networked control systems have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical development of networked control systems. In-depth analysis and discussion is made on sampled-data control, networked control, eventtriggered control. More specifically, existing research methods on networked control systems are summarized. Furthermore, as an active research topic, network-based filtering is reviewed briefly. Finally, some challenging problems are presented to direct the future research. Index Terms—Networked control systems, sampled-data control, event-triggered control, event-based filtering.

I. I NTRODUCTION A networked control system (NCS) is such a system whose control loops are closed through a communication network such that both control signals and feedback signals can be exchanged among system components (sensors, controllers and actuators, etc.), see Fig. 1. A defining feature of an NCS is that it connects cyber space to physical space so that the execution of several tasks is remotely allowed. As a result, an NCS has several advantages over traditional control systems. On the one hand, an NCS may eliminate unnecessary wiring between system components, which means that the complexity and the overall cost in designing and implementing the corresponding control systems can be reduced significantly. On the other hand, an NCS can be modified or upgraded easily if some new or old system components need to be added or removed without major changes in the structure. Therefore, NCSs are found a wide range of applications in areas including industry control [1]. For example, in vehicle industry, extensive wiring in a typical model automobile [2] is reduced by introducing networked control technologies; NCSs are applied successfully in human surveillance systems [3] Manuscript received June 30, 2015; revised November 3, 2015; accepted November 23, 2015. c Copyright⃝2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected]. This work was supported in part by the Australian Research Council Discovery Project under Grants DP1096780 and DP160103567. X.-M. Zhang and Q.-L. Han are with the Griffith School of Engineering, Griffith University, Gold Coast Campus, QLD 4222, Australia (e-mail: [email protected] (X.-M. Zhang) and [email protected] (Q.-L. Han)). X. Yu is with the Platform Technologies Research Institute, RMIT University, Melbourne 3001, Australia (e-mail: [email protected]).

and in many process control engineering systems such as the waster treatment process [2]. However, the use of communication networks usually introduces some challenging issues. To mention a few, the periodic sampling with a high frequency unavoidably brings heavy loads into the network, possibly leading to network congestion; the limited bit rate of communication networks usually leads to network-induced delays and packet dropouts; and quantization errors frequently occur when sampled signals are quantized due to the finite word length of packets. These challenging issues induced by communication networks are generally regarded as sources degrading system performance or destabilizing an NCS, even though some positive effects of network-induced delays on NCSs are shown in [4]. Since NCSs lie at the intersection of control theory and communication theory, analysis of NCSs is more complicated and growing attention is still paid to NCSs up to date. During the past three decades, a great number of results on NCSs are reported. In order to summarize in time the results on NCSs, some survey papers are available in the literature. For example, in 2006, a survey on NCSs was given [5], where the impact of NCSs on traditional large-scale system control methodologies with a related application is reviewed. In 2007, control and communication challenges in networked real-time systems were discussed [6]; and an overview on estimation, analysis, and controller synthesis for NCSs was given [7]. In 2010, some research trends of NCSs were suggested [8]. In 2013, a survey on network-induced constraints in NCSs was carried out [2]. In this paper, different from [2] and [5]–[8], we provide an overview on the theoretical development of networked control systems on the basis of the results reported in the last decade. First, some challenging issues induced from communication networks and their effects on NCSs are discussed in Section II. Second, sampled-data control, networked control and eventtriggered control are reviewed in Section III, where existing research methods on NCSs are summarized. Third, as an active research topic, network-based filtering is reviewed in Section IV. Finally, in Section V, some challenging problems are presented for the future research. II. S OME ISSUES INDUCED FROM NCS S A simple diagram of an NCS is shown in Fig. 2, where control components, i.e. physical plant, sensor, quantizer, controller and zero-order-hold (ZOH), are connected through a communication network. There are several issues induced from an NCS, such as sampling, network-induced delays, packet

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Actuator1

Plant 1

……

Sensor 1

Actuator N

2

Plant N

Sensor N

Shared Communication Network ……

Controller 1 Fig. 1.

Controller N

A networked control system with N control loops Physical plant

Sensor

ZOH

Quantizer

Network

Network Controller

Fig. 2.

A simple networked control system with single loop

dropouts and quantization errors, which are described in the following. A. Sampling Continuous-time signals need to be sampled before they are transmitted through a communication network. There are two methods to sample continuous-time signals: time-triggered sampling and event-triggered sampling. The time-triggered sampling, also called the ‘Riemann sampling’, characterizes ‘when’ an event is triggered, which means that the next sampling instant occurs after the elapse of a fixed time interval. The periodic sampling, or called the uniform sampling, is widely used in early digital control systems because analysis and design of such sampled systems may be simple. When the sampling period approaches to zero, the performance of the sampled control system can approximate that of the corresponding continuous-time system. This means that the smaller the sampling period is taken, the better the performance is achieved for the system. The event-triggered sampling, also called the ‘Lebesgue sampling’, ‘level-crossing sampling’ and ‘magnitude-driven sampling’, etc., characterizes ‘why’ an event is triggered, which means that the next sampling occurs because the signal changes with a specified amount. The event-triggered sampling is a natural way that a human behaves as a controller. One of the motivations of the event-triggered sampling is based on such an observation that, for an NCS, the periodic sampling with a smaller period cannot ensure some better performance because more increasing network-loads usually lead to network traffic congestion, which possibly degrades the system performance. The event-triggered sampling may contribute to significant reduction of network traffic loads. In the last decade, growing attention is paid to event-triggered control for NCSs, see e.g. [9]–[11]. B. Network-induced delays In an NCS, network-induced delays are often caused by several factors, such as limited bandwidth, network traffic and

transmission protocols [12]. There are mainly two kinds of delays: sensor-controller delays and controller-actuator delays. The sensor-controller delays represent the time interval from the instant when a physical signal is sampled to the instant when the corresponding control signal is generated; and the controller-actuator delays indicate how long a control signal is transmitted from the controller to the actuator. Since networkinduced delays depend heavily on variable network conditions [7], they are usually time-varying, random, and unknown but upper-bounded. As a consequence, network-induced delays are commonly modeled as an interval time-varying delay [14], [15] and a Markov chain with known transition probabilities [16], [17], with partially known transition probabilities [18] and with arbitrary switching [19]. The estimation on the upper bound of network-induced delays is an interesting research topic. Traditionally, a network time protocol (NTP) is proposed to measure an end-to-end delay by synchronizing the clocks of computer systems over packet-switching data networks. Due to the inaccuracy of the NTP approach, a clock synchronization procedure protocol was designed [20] to develop the one-way delay measurement methodology for NCSs. Since a time-stamp technique is introduced, the upper bound of network-induced delays may be estimated, see also [21] in detail. Another important issue is to investigate the effects of network-induced delays on NCSs. Among most results on NCSs, network-induced delays are usually considered as a source of system performance degradation or even instability, which means that network-induced delays have the negative effects on NCSs [7]. However, for some systems, such as an offshore platform [22] and a Duffing-Van der Pol’s oscillator, network-induced delays may have the positive effect on system performance. For example, in [23], a network-based dynamic model of an offshore platform is established and a networkbased state feedback control scheme is developed. It is shown through simulation results that both the oscillation amplitudes of the offshore platform and the required control force under the network-based state feedback controller are smaller than those under the nonlinear controller and the dynamic output feedback controller. For a van der Pol-Duffing oscillator, which cannot be stabilized using a delay-free position feedback controller, it is shown [4] that some stable and satisfactory tracking control performance can be achieved by intentionally introducing proper network-induced delays between the oscillator and the controller. It is of significance in theory and in practice to pay more attention to investigating the positive effects of network-induced delays on NCSs. C. Packet dropouts Packet dropout phenomenon is an important issue induced from NCSs. Because of limited network bandwidth, data packets may be dropped out during their transmission in a communication network. Generally, there are two types of packet dropouts. • Network-induced packet dropouts. When the network is in some worst conditions, such as overloaded network traffic and the occurrence of transmission time-outs and



transmission errors in the communication network, packet dropouts may occur, even though network protocols are equipped with transmission retry mechanisms. Because in this case the packet dropout is caused by the communication network itself, we call this kind of packet dropout the “network-induced packet dropout”; and • Active packet dropouts. In some cases, where a packet sent earlier arrives at its destination later, packet disorder phenomena appear. Although disordered packets can be delivered eventually under some reliable transmission protocols, such as TCP, they are not very useful for analysis and design of NCSs because the signals carried in the disordered packets are out-of-date. Therefore, one should discard those ‘disordered’ packets actively. We call this kind of packet dropout the “active packet dropout”. Some effective methods, such as logical ZOH mechanisms [24] and message rejection [25], are presented to carry out active packet dropouts. Generally, packet dropouts exhibit random features because network dynamics usually change in a random way. In consequence, packet dropouts are treated as a Markov packetdropout process [26] or a Bernoulli distributed white sequence [27]. However, packet dropouts together with networkinduced delays are modeled as an time-varying delay both in continuous-time domain [28] and in discrete-time domain [25]. D. Quantization errors In NCSs, signals are usually quantized by a quantizer before they are transmitted via a communication network. A quantizer can be regarded as a class of nonlinear mappings, which map different segments of R (the real set) to different levels. The number of quantization levels is closely related to the information flow between the physical plant and the filter. Because of the finite word length, quantization errors are unavoidable, which have the negative effects on the system performance of NCSs. It is shown [29] that quantization errors can be treated with a sector-bounded uncertainty or nonlinearity. This means that robust analysis methods can be applied to investigate the effects of quantization errors on NCSs. Therefore, a number of results on this issue are reported in the literature. For an NCS subject to quantization and stochastic packet dropouts, meansquare stability is studied [30] in the case of a logarithmic quantizer with an infinite number of quantization levels, where some relationship between the level of quantization and the packet dropout probability is disclosed. For an NCS subject to packet dropouts and finite-level quantization, effects of the quantizer step size and the maximal number of consecutive packet dropouts on the disturbance attenuation level of the NCS are investigated [31]. A unified and generalized framework is proposed for analysis and design of networked and quantized control systems via an emulation-like approach, which offers a different way to study quantization effects on NCSs [32]. III.

SAMPLED - DATA CONTROL , NETWORKED CONTROL AND EVENT- TRIGGERED CONTROL

In the last decade, a large number of results focus on control of NCSs by taking one, two or more issues mentioned in the

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previous section into account. A review on them is given in the following subsections. A. Sampled-data control When control components are located in the same place, the NCS can be regarded as a digital control system. Sampleddata control theory for digital control systems has been well established since the late of 1950s. An input delay approach to sampled-data control, which was originally proposed in 1988 [33], gains resurgent attention because of the advent of a linear matrix inequality technique and the boost of network control technologies. Employing the input delay approach, the maximum upper bound of two consecutive samplings and corresponding controller can be obtained easily. In the last decade, some significant progress is made in sampled-data control using the input delay approach. Consider the following sampled-data system described by { x(t) ˙ = Ax(t) + Bu(t), x(t0 ) = x0 (1) u(t) = Kx(tk ), tk ≤ t < tk+1 where x(t) ∈ Rn and u(t) ∈ Rm are the system state vector and the control input vector, respectively; and x0 is an initial condition. {t1 , t2 , · · · , tk , · · · } is a sampling sequence satisfying tk < tk+1 , limk→∞ tk = ∞ and supk {tk+1 − tk } ≤ hu for some known hu > 0. The input delay approach is to model the system (1) as the continuous-time system described by [34] { x(t) ˙ = Ax(t) + BKx(t − τ (t)), tk ≤ t < tk+1 (2) x(θ) = x0 for θ = t0 ; 0 for θ < t0 with a piecewise time-varying delay τ (t) := t − tk , tk ≤ t < tk+1 satisfying 0 ≤ τ (t) ≤ hu , ∀t ≥ t0 . Thus, based on this model, the Lyapunov-Krasovskii functional approach can be employed to formulate some stability criteria such that the admissible upper bound of hu and the corresponding controller gain K can be calculated in terms of solutions to some linear matrix inequalities. A number of significant results using the input delay approach can be referred to [34] and [35]. However, some comment on the input delay approach is made in [36], where it is pointed out that some available information about the actual sampling patten are neglected [34], leading to conservative stability criteria. Based on the scaled small-gain theorem, the input delay approach is revised [36] using a tighter upper bound on the L2 induced-norm of ∫t the operator ∆sh defined as ξ = ∆sh η ⇐⇒ ξ(t) = tk η(s)ds. By exploiting passivity-type property of ∆sh instead of the L2 induced-norm, the stability criteria [36] are further improved by [37]. Recalling the result [34], it is found that a key feature, i.e. τ˙ (t) = 1 ∀t ≥ t0 , a.e., of the piecewise time-varying delay τ (t) is not used in the stability analysis of the system (2). This accounts for the conservatism of the obtained results. Motivated by the discontinuous Lyapunov function method proposed [38], the input delay approach is refined [39] by introducing a proper time-dependent Lyapunov-Krasovskii



functional including the following term [ ]T [ ] x(t) x(t) Vtd (t, x(t)) := (tk+1 −tk −τ (t)) M x(tk ) x(tk )

4

(3)

where M is a symmetric real matrix. Simulation results illustrate that the time-dependent Lyapunov-Krasovskii functional method can produce much less conservative stability criteria than those [36]–[38]. Therefore, the time-dependent Lyapunov-Krasovskii functional method receives increasing attention of researchers in the field of control. To mention a few, the time-dependent Lyapunov-Krsovskii functional method, together with the Wirtinger-based integral inequality, is used to derive some less conservative stability criteria for sampled-data systems [40]; by replacing tk+1 − tk − τ (t), i.e. tk+1 − t, in (3) with (tk+1 − t)(t − tk ), stability for sampled-data systems with state quantization is analyzed [41]; the time-dependent Lyapunov-Krsovskii functional method is employed to co-design event-triggered transmission schemes and L2 controllers for sampled-data systems [42]. In addition to the input delay approach, a number of approaches are proposed for analysis and control of sampleddata systems in recent years [43]. The sampled-data system under consideration is modeled as a continuous-time system, while the stability analysis is made using the discrete-time Lyapunov theorem [44]. An impulsive system approach is introduced to consider the stability of sampled-data systems using a discontinuous Lyapunov functional [38]. This approach is improved in [45], where the chosen Lyapunov functional is continuous at impulse times but not necessarily positive definite inside the impulse intervals. A continuous time-delay system model with stochastic parameters satisfying Bernoulli distribution is proposed for sampled-data systems with multi sampling rates [46]. B. Networked control Network-induced delays and packet dropouts are still challenging issues for NCSs. In the last decade, depending on how to deal with network-induced delays and packet dropouts, a number of methods are developed on stability analysis and control design for NCSs. In the following, we provide a brief review on them. 1) A time-delay system approach: A time-delay system approach is motivated by the input delay approach for sampleddata systems [15]. By the time-delay system approach, the closed-loop system is modeled as a system with a time-varying delay. The information on the sensor-controller delay, the controller-actuator delay and the number of consecutive packet dropouts is implicitly included in the upper bound of the input delay. Denote the updating instants of the ZOH as tk (k = 1, 2, · · · ). At the time instant tk , the time-stamp of the packet transmitted successfully from the sensor to the ZOH is assumed to be ik . Thus, the closed-loop system can be given by x(t) ˙ = Ax(t) + BKx(t − τ1 (t)), t ∈ [tk , tk+1 )

(4)

where τ1 (t) = t − ik h is called the input delay and h is the sampling period. It is clear that τ1 (t) is a piecewise timevarying delay satisfying τmin ≤ τ1 (t) < τmax , where τmin := mink {tk − ik h} and τmax := maxk {(tk+1 − tk ) + (tk − ik h)}. With this timing mechanism, the network-induced delays can be given as tk − ik h (k = 1, 2, · · · ), while the number of consecutive packet dropouts cannot be expressed explicitly, but closely related to tk+1 − tk . Hence, the information on network-induced delays and the number of consecutive packet dropouts is included in the piecewise time-varying delay τ1 (t). However, packet disorder phenomena are not considered in the timing mechanism. In [24], a logical ZOH is introduced to choose those newest data packets to actuate the physical plant. With the mechanism of the logical ZOH, disordered data packets are dropped out actively. As a result, both the network-induced delays and the number of consecutive packet dropouts can be explicitly worked out, respectively. In fact, the network-induced delay is given by τk := tk − ik h, and the number of packet dropouts between tk and tk+1 can be calculated as ℓk+1 := ik+1 −ik −1. Moreover, the upper bound τmax of τ1 (t) can be re-expressed as τmax = maxk {(ℓk+1 + 1)h + τk+1 }, which clearly shows that the upper bound of the piecewise time-varying delay τ1 (t) depends on the admissible maximum number of consecutive packet dropouts, the admissible maximum network-induced delays and the sampling period. Another important aspect regarding the time-delay system approach is the proper Lyapunov-Krasovskii functional for the system (4) with a piecewise time-varying delay τ1 (t). It is natural to use Lyapunov-Krasovskii function (LKF) method to derive an admissible upper bound τmax such that the certain system performance can be ensured. A number of results are based on the following LKF, or its modifications by adding some terms including information on the lower bound τmin and the upper bound τmax [15], [28] ∫ 0 ∫ t V (t, xt ) = xT (t)P x(t)+ x˙ T (s)Rx(s)dsdθ ˙ (5) −τmax t+θ

where P and R are positive definite symmetric matrices. Apparently, the piecewise characteristic of the time-varying delay τ1 (t) is not reflected from the LKF V (t, xt ). Thus, the obtained results are somewhat conservative [36]. Motivated by this observation, a time-dependent LKF is constructed for sampled-data systems [39], and then is extended to NCSs [47], which is similar to the one in (3). It is shown that timedependent LKFs can produce some less conservative results than those using time-independent LKFs such as (5). The time-delay system approach in the discrete-time domain is also employed to the analysis and control of discretetime NCSs. One can refer to [24]. Notice that the timedelay system approach aims to derive a maximum delay upper bound such that the system can tolerate and maintain some desired system performance. However, the maximum delay upper bound indicates the worst case of network-induced delays and the number of consecutive packet dropouts that the NCS can tolerate. If the worst case occurs rarely, the results derived from the time-delay system approach are certainly conservative, which is shown through some examples in [48].



2) A Markovian jumping system approach: A Markovian jumping system approach is developed for analysis and control of NCSs. This approach is usually applied in the discretetime domain. Taking network-induced delays into account, the closed-loop NCS is modeled as a Markovian jumping system, and an LQG optimal controller design method is then used [49]. With a known packet dropout rate, the closed-loop NCS is modeled as a Markovian jumping system such that suitable H∞ controllers can be designed [50]. The stabilization issue on NCSs is discussed using a Markovian jumping system approach [26]. It is worth pointing out that the notion of packet-dropout dependent Lyapunov functions is introduced [26], leading to some less conservative stability criteria. A significant Markovian jumping system model is proposed for NCSs with network-induced delays [17]. The key idea is to model sensor-controller delays and controller-actuator delays as two different homogeneous Markov chains, and hence the closed-loop NCS is modeled as a Markovian jumping system with two modes characterized by two Markov chains. A remarkable characteristic is that the controller gain is networkinduced-delay-dependent, while it is assumed to be a constant matrix in most results on NCSs. Furthermore, a necessary and sufficient condition on the existence of stabilizing state feedback controllers is presented [17], which is extended to output feedback control and robust mixed H2 /H∞ control [16] for NCSs with networked-induced delays. In the continuous-time domain, relatively few results on NCSs are reported using a Markovian jumping system approach. In [51], a Markovian jumping system approach in the continuous-time domain is proposed for state feedback control of NCSs with networked-induced delays. Similar to [17], sensor-controller delays and controller-actuator delays are modeled as two different Markov chains, while modes of two Markov chains are defined as different network load conditions. For a different network load condition, the networkinduced delay is upper-bounded by a different constant. Such a definition of the Markov chains needs the assumption that instantaneous states of the network load conditions should be accessible by the controller and the sensor, which is unreality. For instance, at the current time instant, it is impossible for the controller to know the future state of the network load condition in the channel from the controller to the actuator. By decomposing the delay interval into several subintervals, a Markovian jumping model is proposed for network-based filtering error systems [52]. The proposed method can be adapted for stability analysis and control design of NCSs. 3) A switched system approach: A switched system approach with arbitrary switching is used in the analysis and control of NCSs. There are a number of ways to model an NCS as a switched system with arbitrary switching, see, e.g. [53]– [58]. Most of them are based on the discrete-time domain, while few of them on the continuous-time domain. For an NCS with a continuous-time physical plant, some discrete-time switched models of the closed-loop NCS are proposed in different cases. Under the assumption that the control signal is time-varying (not constant) within a sampling period, a discrete-time switched model is established [57], where the sampling period is divided into a number of subintervals on

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which the controller reads its buffer at a higher frequency than the sampling frequency. With this model, stability and persistent disturbance attenuation are investigated for NCSs with network-induced delays less than one sampling period. If network-induced delays are less than one sampling period, a discrete time-delay switched model is derived for the closedloop NCS with finite consecutive packet dropouts [53]. By employing the average dwell time technique, some quantitative relation between the packet dropout rate and stability of the NCS is established [53]. Note that the constraint on networkinduced delays less than one period is not easily satisfied for some NCSs. In order to remove this constraint, efforts are made, see, e.g. [19], [54], [55]. In [19], the NCS with networkinduced delays is modeled as a discrete-time switched system with multiple state delays and with both stable subsystems and unstable subsystems. The network-induced delay is timevarying and not necessarily less than one sampling period. By using an event-based discrete-time representation of the continuous-time physical plant, the NCS is modeled as a switched system with arbitrary switching [55], in which a long network-induced delay and non-uniform sampling are allowed. The physical plant is equipped with sensors and actuators that are grouped into a number of nodes [54]. At each transmission instant, a switching indicator is used to choose only one certain node to access the network and transmit its corresponding data packets. In this way, a discrete-time switched linear uncertain system model is established, where the constraint on networkinduced delays less than one period is removed. For an NCS with a discrete-time physical plant, a switched system approach is proposed [59] to deal with the output feedback control of NCSs with packet dropouts. Both networks from the sensor to the controller and from the controller to the actuator are modeled as two switches indicating that a data packet is dropped out or not, while network-induced delays are not taken into account. Using an iterative technique, a switched model for NCSs with packet dropouts and one step delay is presented to design state feedback controllers. The proposed method is improved by [26], where network-induced delays may be two steps or more, but known to be constant. In the continuous-time domain, a switched system approach is also studied in [58] and [56] for NCSs with networkinduced delays. By introducing a switching function related to the variation of network-induced delays, the closed-loop NCS is modeled as a time-delay switched system with two switching modes. A significant feature is that each mode has a different controller gain. Based on the time-delay switched system model, together with the average dwell time technique, stability analysis is made [58], and exponential stabilization is investigated [56]. Another related system type is the sliding mode control systems where switching of control is assumed to be infinitely fast. Of course, this assumption may not be satisfied when sliding mode control systems are discretized using ZOH or Euler’s discretization [60]. This type of NCSs may result in severe chattering [61] or even discretization chaos [62]. Sophisticated control methods are required. For example, in [63], a delay-dependent sufficient condition is obtained for asymptotic stability, and in [64], the idea of delay fractioning



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is used with a new Lyapunov-Krasovskii functional to derive sufficient conditions for asymptotical stability. Nevertheless, control of this type NCS is less attended due to the difficulty in dealing with discontinuity in NCSs where the usual dwell time conditions for switched NCS do not hold. New methodologies are needed to deal with the special class of NCSs effectively. 4) A stochastic system approach: In an NCS, networkinduced delays and packet dropouts sometimes exhibit random characteristics [49], [65]. The stochastic system method provides a powerful tool for analysis and control of NCSs with random network-induced delays and random packet dropouts. A number of results using stochastic system approaches are reported in the literature. To mention a few, by describing network-induced delays as a sequence of continuous random variables, stability is studied for NCSs [66]; Some input-output stability criteria are derived for a general class of nonlinear NCSs with exogenous disturbances using stochastic protocols in the presence of random network-induced delays and packet dropouts [67]. For longer network-induced delays with certain probabilities, a stochastic system approach is proposed [48] in the discrete-time domain. This approach can produce some less conservative stability criteria than those using a time-delay system approach. To make it clear, consider the following discrete time-delay system model of NCSs [24] x(k + 1) = Ax(k) + BKx(k − d(k))

(6)

where the time-varying delay d(k) is a positive integer taking values in {τ1 , · · · , τq } with τj < τj+1 (j = 1, 2, · · ·, q − 1). If the stochastic process {d(k)} is independently ∑ and identicallyq distributed with Pr{d(k) = τj } = αj , where j=1 αj = 1, a stochastic description of (6) can be given by ∑q x(k + 1) = Ax(k) + j=1 Id(k)=τj BKx(k − τj ) (7) where Id(k)=τj is an indicator function: Id(k)=τj = 1 if d(k) = τj ; Id(k)=τj = 0 otherwise. The time-delay system approach based on (6) aims to derive an admissible maximum τq for given τ1 such that the system is asymptotically stable, while the stochastic system approach based on (7) is to check if the system is mean-square stable for some certain probabilities αj related to the delay values τj (j = 1, 2, · · · , q). Therefore, for some network-induced delays with smaller probabilities, it is possible that the time-delay system approach cannot draw any conclusion on stability of the NCS, but the stochastic system approach can, which is illustrated through some examples in [48]. For output feedback control of NCSs subject to random network-induced delays, a useful stochastic system approach can be presented by following the idea in [68]. Introduce a stochastic variable to describe the system measurement y˜(k) that the controller receives at the time instant k as { y(k) = Cx(k) + Bw w(k) (8) y˜(k) = (1 − σk )y(k) + σk y(k − 1) where y(k) is the system measurement, and {σk } is a Bernoulli distributed white sequence. From (8), it is clear to see that, y˜(k) is either y(k) if the data packet containing the signal

y(k) arrives at the controller before the kth sampling instant or y(k − 1) if the arrival takes place after the kth sampling instant. Based on the model (8), a stochastic system approach is proposed to deal with H∞ control [27], [65], observer-based networked control [69], and H∞ filtering [70] for NCSs with random network-induced delays and packet dropouts. 5) An impulsive system approach: An NCS can be modeled as an impulsive system. For example, based on the time-delay system (4), an impulsive presentation can be easily formulated. In fact, denote z(t) := x(ik ) for tk ≤ t < tk+1 and ζ(t) := col{x(t), z(t)}. Then [ ]  A BK  ˙  ζ(t) = ζ(t), tk ≤ t < tk+1  0 0 [ − ] (9)  x(tk+1 )   ζ(tk+1 ) = , k∈N x(ik+1 ) which is an impulsive system model of the NCS. The impulsive system approach is studied [38] in detail for NCSs. By choosing a discontinuous Lyapunov functional, some novel exponential stability criteria are obtained, which are less conservative than those using the time-delay system approach [15]. By introducing an improved discontinuous Lyapunov functional, the impulsive system approach is employed to deal with the input-output stability for NCSs [71]. Moreover, using the impulsive system approach, tracking control for NCSs is discussed [72]. C. Event-triggered control In event-triggered control, the execution of control tasks is determined by the occurrence of an event rather than the elapse of a fix time period in time-triggered control. Some significant advantages of event-triggered control were discussed [73], from which it is shown that event-triggered control can obtain large reductions in resource utilization with minor control performance degradation. Motivated by those advantages, the first systematic design of event-based implementations of stabilizing feedback control laws is done [10]. Since then, event-triggered control is received a lot of attention, and becomes a hot research topic in the field of NCSs, see, e.g. [9], [11], [74]. A key point to event-triggered control is to design a suitable triggering condition that determines whether or not a control task is executed. Suppose that the state x(t) of the physical plant is available. Then some existing triggering conditions are given as [9]:

∥x(t) − x(tk )∥ ≤ δ

[10]:

∥x(t) − x(tk )∥ ≤ δ∥x(t)∥

[11]:

∥M

1/2

[x(t) − x(tk )]∥ ≤ δ∥M

(10) (11) 1/2

x(tk )∥

(12)

where tk denotes the time instant when the last control task is executed (or called the last event time instant); M > 0 is a weighting matrix and δ > 0 is a threshold. Thus, the next event time instant tk+1 can be given, respectively, as tk+1 = inf{t > tk |∥x(t) − x(tk )∥ > δ} tk+1 = inf{t > tk |∥x(t) − x(tk )∥ > δ∥x(t)∥}



1

7

1

tk+1 = inf{t > tk |∥M 2 [x(t)−x(tk )]∥ > δ∥M 2 x(tk )∥} Apparently, the triggered conditions in (10)-(12) depend on the instantaneous state x(t), which means that extra hardware is required to monitor the system state constantly [9], [10]. In order to avoid extra hardware, self-triggered control is proposed [11]. In self-triggered control, an estimation of the next event time instant is made, and the dedicated hardware used in event-triggered control schemes is replaced by online computation based on previously received data and knowledge on the plant dynamics. In either event-triggered control or self-triggered control, two important issues should be addressed: the minimal interevent time and the control design. i) The minimal interevent time τ ∗ can be defined as τ ∗ := min{tk+1 − tk |k = 1, 2, · · · }. If τ ∗ is zero, infinite events occur within a finite time interval (Zeno behavior), which means that the eventand self-triggered control cannot be implemented on a digital platform. Hence, a certain positive minimal inter-event time should be ensured in either event- or self-triggered control. It is proven [10] that the positive minimal inter-event time is guaranteed to exist for linear physical plants without external disturbances and linear state feedback controllers, while it is shown [74] that the positive minimal inter-event time may be zero in case output feedback controllers are used in a similar setup. More especially, properties of inter-event times for event-triggered control systems are investigated [75], and it is proven that for some classes of systems, even though a positive minimal inter-event time can be ensured in the absence of external disturbances, it possibly becomes zero for arbitrary small external disturbances. Thus, it is not an easy task to ensure that an event-triggered control system has a positive minimal inter-event time especially in the presence of external disturbances. ii) The control design should be related closely to the triggering condition predefined. However, in a number of results concerning event- or self-triggered control, the controller is usually given a priori, which means that the control design does not take the event-triggered nature into account. Regarding the above two issues, most of results focus on the first one, while the second issue is seldom involved. In 2013, an effective event-triggered transmission scheme was proposed [76] to address the above two issues. Let system states be sampled in a fix period h > 0. Whether or not the current sampled signal should be transmitted to the controller is determined by a predefined triggering condition, which is given as eT (i0 , j)Ωe(i0 , j) ≤ σxT ((i0 + j)h)Ωx((i0 + j)h)

(13)

where e(i0 , j) = x((i0 + j)h) − x(i0 h) (j = 1, 2, · · · ); i0 h stands for the last event time; Ω > 0 is a weighting matrix and σ ∈ (0, 1) is a threshold parameter. From the triggering condition (13), a clear observation is that events are triggered at the sampling instants, which leads to a guaranteed minimal inter-event time τ ∗ ≥ h > 0 even though external disturbances are imposed on the physical plant. Moreover, under the proposed event-triggering scheme, the closed-loop system with a state feedback controller u(t) = Kx(t) can be modeled as a time-delay system. For simplicity of presentation, we suppose

that the communication network is reliable with no networkinduced delays and no packet dropouts. At the time instant kh (k = 1, 2, · · · ), the latest event time is denoted by tk . Then u(t) = Kx(tk ), Set

{

t ∈ [kh, (k + 1)h)

τ (t) = t − kh φ(t) = x(tk ) − x(kh)

t ∈ [kh, (k + 1)h)

(14)

(15)

which leads to u(t) = K[x(t − τ (t)) + φ(t)],

t ∈ [kh, (k + 1)h)

(16)

where τ (t) and φ(t) satisfy, respectively, 0 ≤ τ (t) < h φT (t)Ωφ(t) ≤ σxT (t − τ (t))Ωx(t − τ (t))

(17)

Therefore, the closed-loop system can be described as, for t ∈ [kh, (k + 1)h) x(t) ˙ = Ax(t) + BKx(t − τ (t)) + BKφ(t)

(18)

which is a time-delay system subject to (17). By employing Lyapnuov-Krasovskii functional method, together with Sprocedure, the controller gain K and the weighting matrix Ω can be designed for a given threshold σ ∈ (0, 1). If taking network-induced delays into account, a time-delay system model similar to (18) can also be established, and one can see [76] in detail. Due to the above advantages, the event-triggered transmission scheme comes to the fore. Under this scheme, a number of issues for NCSs is addressed, e.g. L2 analysis and control design [42], tracking control [79], dynamic output feedback control [77], [78], H∞ filtering [80] and consensus of multi-agent systems [81]. However, if taking packet dropouts into account, the delay-system model (18) is inapplicable, and up to date, no any model is reported for this case. IV. N ETWORK - BASED FILTERING Control and filtering are two fundamental issues in the field of NCSs. In some circumstance, a filter is similar to a dynamic output feedback controller. In this section, we focus on network-based filtering. The filtering problem is also called a state estimation problem. For a dynamic system in the presence of process noise, the objective of the filtering problem is to estimate system states (or a linear combination of them) using noisy output measurements. The celebrated Kalman filtering (or called H2 filtering) approach provides a recursive algorithm to minimize the variance of the state estimation error when the noise is random with known statistical properties or has a known power spectral density. However, the Kalman filtering approach is sensitive to uncertainties in the exogenous noise signals. If statistical properties of the noise are unknown, the Kalman filtering approach is inapplicable. In this case, an alternative approach, called H∞ filtering, can be applied. The H∞ filtering approach was first considered for the scalar case using polynomial techniques [82]. In comparison with Kalman filtering, a significant characteristic of H∞ filtering is that only upper bounds on the spectral density of the input



w

8

z

Physical Plant

−z f

y Sampler Fig. 3.

e

Network

ZOH

y%

Filter

A simple diagram for network-based filtering

and measurement noises need to be known. As a result, H∞ filtering attracts considerable attention since the late of 1980s. Specifically, a number of issues related to H∞ filtering are developed, for example, mixed H2 /H∞ filtering, nonlinear H∞ filtering, adaptive H∞ filtering, reduced-order H∞ filtering, Markov jumping H∞ filtering, stochastic H∞ filtering, and H∞ filtering for time-delay systems, which greatly enriches the H∞ filtering theory. With the rapid development of communication technology, communication networks are used to transmit signals between components of a control system because of its significant advantages. In modern H∞ filtering setting, one usually use a communication network to transmit signals from a physical plant to a filter, which leads to network-based H∞ filtering. A simple diagram for network-based H∞ filtering is shown in Fig. 3. Network-based H∞ filtering allows the physical plant and the filter to be located at different places, which makes remote H∞ filtering possible. Thus, compared with the traditional H∞ filtering, network-based H∞ filtering has wider potential application scopes. However, unfavourable constraints caused by limited bandwidth make the networkbased H∞ filtering problem more complicated. An important concern is that, in Fig. 3, the input signals of the filter y˜(t) are no longer equal to the output measurements y(t) of the physical plant at any time t ≥ 0 because of network-induced delays and packet dropouts. In order to cope with this problem, a number of results are reported in the literature. In the following, we present a brief review on this issue from two aspects: in the continuous-time domain and in the discretetime domain. A. Network-based filtering in the continuous-time domain In the continuous-time domain, the network-based H∞ filtering issue is addressed in [52], [83], [84]. By taking network-induced delays and packet dropouts into account, the filtering error system is modeled as a linear system with an interval time-varying delay [83]. By employing LyapunovKrasovskii functional approach, sufficient conditions on the existence of H∞ filters are derived. By taking into account network-induced delays, packet dropouts and quantization errors, the filtering error system is modeled as an uncertain linear system with two additive time-delays [84]. Both quadratic and parameter-dependent stability analysis approaches are exploited to provide alternatives for designing robust H∞ filters with different degrees of conservativeness and computational complexity. By taking quantization errors into account, the filtering error system is modeled as a stochastic system with a piecewise time-varying delay [85]. A time-dependent

Lyapunov function approach is introduced to produce some less conservative sufficient conditions on the existence of suitable H∞ filters. However, network-induced delays and packet dropouts are not considered in [85]. The time-delay system approach proposed in [83] and [84] is of somewhat limitation. The first is that the packet disorder phenomena are not taken into consideration. During data transmission through a communication network, a disordered data packet usually induces a long transmission delay, which often degrades the system performance. On the other hand, a disordered packet carries out-of-date signals, so it is unreasonable to be used for H∞ estimation. The second is that the time-delay system approach aims at deriving an admissible maximum upper delay bound, which indicates the worst case of the network-induced delays and the number of consecutive packet dropouts. However, if the worst case occurs rarely, the obtained results are more conservative from the H∞ performance point of view. The third is that the designed H∞ filter just has one mode, which cannot reflect the variation characteristic of the network-induced delay and packet dropouts during signal transmission. Based on the above observations, a Markov jumping model approach with a jumping-like trigger is proposed [52] to study the networkbased H∞ filtering. Some distinctive characteristics are listed as follows. First, a logical ZOH is introduced to select the newest data packets to be transmitted. With the logical ZOH, those disordered packets are dropped out actively. Moreover, both network-induced delays and the number of consecutive packet dropouts can be expressed explicitly. Second, a Markov jumping filter model is presented. Denote by {t1 , t2 , · · · , tk , · · · } the sequence of instants when the logic ZOH updates its store. Then the input signal of the filter can be given by y˜(t) = y(t − τ (t)), tk ≤ t < tk+1 where the time-varying delay h(t) satisfies h1 ≤ τ (t) ≤ h2 , τ˙ (t) = 1, ∀t ≥ 0, a.e. Decompose the delay interval [h1 , h2 ] uniformly into N subintervals denoted by Ij , (j ∈ S , {1, 2, · · · , N }). For any t ≥ 0, there exists an i ∈ S such that τ (t) ∈ Ii . For a given ∆ > 0, τ (t + ∆) may jump into another interval Ij (j ∈ S), which means that τ (t + ∆) ∈ Ij |τ (t) ∈ Ii . Thus, we can use a Markov process to describe the above jumping of τ (t). Suppose that {r(t), t ≥ 0} is a continuous-time, discrete-state homogeneous Markov process taking values in a finite set S. The Markov process {r(t), t ≥ 0} governs the switching of τ (t) among the different subintervals. The transition rates are given by { λij ∆+o(∆), j ̸= i; Pr{r(t+∆) = j|r(t) = i} = (19) 1+λii ∆+o(∆), j = i. = 0, λij ≥ 0 (i ̸= j) and λii = where lim∆→0 o(∆) ∆ ∑N − j=1,j̸=i λij . Therefore, the H∞ filter can be designed of



the following form { x˙ f (t) = Af (r(t))xf (t) + Bf (r(t))y(t−τr(t) (t)) zf (t) = Cf (r(t))xf (t) + Df (r(t))y(t−τr(t) (t))

9

(20)

where Af (r(t)), Bf (r(t)), Cf (r(t)) and Df (r(t)) are filter parameters to be determined. It is clear to see that the filter in (20) has N modes, reflecting the variation in nature of the network-induced delays and packet dropouts, which is different from that [83], [84]. Third, a logic jumping-like trigger is designed to simulate the switching of the Markov process. This logic trigger is embedded in the logic ZOH to share its data. The mechanism of the logic jumping-like trigger is described [52] in detail. With this mechanism, the transition rate in (19) can be approximated by { π ∑N ij , j ̸= i ℓ=1 πℓj ∆ λij ≈ (21) π 1 ii ∑N − ∆, j = i π ∆ ℓ=1

ℓj

where the elements πij (i, j ∈ S) stored in the logic trigger indicate the total number of the switching from mode i to mode j during a certain time interval.

This model is similar to the one in (22). However, both multiple packet dropouts and quantization effects are taken into consideration in the model in (23). Nevertheless, the effects of network-induced delays are also ignored. Moreover, the measurement output yk is not corrupted by noise, which does not meet the requirement of H∞ filtering. iii) y˜k is modeled as [90] ∑q y˜k = I{τk =0} C0 x(k)+ j=1 I{τk =dj } x(k−dj )+Dw(k) (24) where w(k) is the disturbance noise; dj j = 1, 2, · · · , q are some known time-delays; τk is a stochastic variable; I{τk =0} and I{τk =dj } are some indicator functions with E {I{τk =0} } = prob{τ ∑kq= 0} = p0 and E {I{τk =dj } } = prob{τk = dj } = pj with j=0 pj ≤ 1. This model with q = 0 is used to describe the measurement missing [91]. It is claimed [90] that this model provides a unified representation to account for both the random communication delays and stochastic data missing. However, it is not clear how the measurement noise w(k) imposed on yk is transmitted through a communication network. Moreover, how to get the probabilities pj is not involved [90]. iv) y˜k is modeled as [86] y˜k ∈ {yk , yk−1 , · · · , yk−N˜ }

B. Network-based filtering in the discrete-time domain In the discrete-time domain, the network-based H∞ filtering issue is gained more attention than that in the continuous-time domain, and a number of results are derived, see, e.g. [18], [19], [70], [86]–[90]. In this scenario, both the physical plant and the filter are described as discrete-time systems. Denote by yk and y˜k the measurement of the physical plant and the input of the filter, respectively. Then the models of y˜k , which are commonly used in the literature, can be summarized as follows. i) The input of the filter y˜k is modeled as [89] y˜k = θk yk + (1 − θk )˜ yk−1

(22)

where the random variable θk is a Bernoulli distributed white sequence taking values in {0, 1} with prob{θk = 1} = E {θk } = α, (0 ≤ α ≤ 1). From this model, it is clear that the packet may be dropped out with a known probability α. If the packet is lost, y˜k = y˜k−1 ; otherwise, y˜k = yk . It is worth pointing out that this model can describe multiple packet dropouts. In fact, if θk = θk−1 = 0 and θk−2 = 1, then y˜k = y˜k−1 = y˜k−2 = yk−2 . Thus, two consecutive packets, i.e. yk and yk−1 , are dropped out. Up to date, much attention is paid to this model to deal with packet dropouts, see, e.g. [88]. However, this model does not take network-induced delays into account, which limits the application scopes of the obtained results. ii) The input of the filter y˜k is modeled as [87] { yk = Cx(k) (23) y˜k = (1 − θk )qµk (yk ) + θk qµk −ℓk (yk−ℓk ) where x(k) is the system state; qµ (·) is a dynamic quantizer with the dynamic scaling µ; ℓk is the number of consecutive packet dropouts and θk is of the same meaning as that in (22).

(25)

˜ is the upper bound of random transmission delays. where N The advantage of this model is that the transmission delays of data packets are allowed to be greater than one sampling period. Based on this description of y˜k , the random transmission delays are modeled as a Markov chain, and the filtering error system is modeled as a Markvoian switched system with state delays. However, the filter to be designed in [86] has only one mode, irrespective of the variation of network-induced delays. This model is also employed to deal with H∞ filtering for networked systems with multiple time-varying transmissions and random packet dropouts [92]. v) By introducing a logic data packet processor, which is motivated from [24], to choose the newest signals to actuate the filter, the input signal y˜k can be given by [18] y˜k = yk−rk

(26)

where rk = k − ik with ik being the time-stamp of the newest data packet that the logic data packet processor receives at the time instant k. Compared with above models (22)-(25), the model (26) has the following advantages: i) network-induced delays and packet dropouts are handled simultaneously; and ii) the filter to be designed has several modes, reflecting the variation of network-induced delays and packet dropouts. Moreover, the transmission probabilities from one mode to another mode do not need to be known a priori. However, effects of quantization errors are not considered in [18]. From the above mentioned, one can see that most of results on network-based filtering take some network constraints into account either in the continuous-time domain or in the discrete-time domain. How to construct a unified framework to analyze the effects of those network constraints simultaneously on the performance of the filtering error system is of theoretical and practical significance. An important observation is that



10

the network-based filtering issue is usually addressed just from the control point of view, rather than from the communication network point of view. It may be helpful for solutions to the challenging issue if one can combine control theory with communication theory to investigate the problem of networkbased filtering. V. S OME CHALLENGING PROBLEMS Although a great number of results on NCSs are reported in the literature, there are still a number of challenging problems to be solved out, some of them are presented as follows. • It is challenging to model an NCS such that networkinduced delays and packet dropouts are explicitly dependent. From Section III.B, by introducing a logic ZOH to an NCS, network-induced delays and packet dropouts can be expressed explicitly. Consequently, it is believed that such a novel model for NCSs is possible. With the model, effects of network-induced delays and packet dropouts on NCSs can be clearly revealed, respectively. However, in the existing models, network-induced delays and packet dropouts are lumped together. As a result, it is hard to distinguish their respective effects on NCSs; • For industrial control and applications of NCSs, it is interesting to investigate possible positive effects of network-induced delays and packet dropouts on NCSs. The positive effects of network-induced delays are shown [4] through the output tracking control of a val der PolDuffing oscillator. From the discussion on quenching [93], some unstable systems with constant time-delay may be stable if the delay becomes time-varying. Hence, proper network-induced delays and packet dropouts may make an unstable system with constant time-delay stable in network environments, which is still challenging; • In event-triggered control, it is of significance to design event-triggered controllers by taking the following triggering condition into account ∫ t (27) ∥x(s) − x(tk )∥ds ≤ σ tk

•

where σ > 0 is a threshold and tk denotes the last event time. The triggering condition (27) characterizes that once the accumulation error of the system state reaches the threshold σ, an event is triggered. It is more useful than those triggering conditions (10)-(12) from two points of view: i) some system performance is usually defined as an integral form similar to (27); and ii) in some situation, for example, when the system approaches to its equilibrium state, no events are triggered after a certain time instant if using the triggering conditions (10)-(12), because the error ||x(t) − x(tk )|| is not large enough to trigger the next event, while it is the opposite if using the triggering condition (27). The condition (27) is proposed originally in [94], but no design issue is addressed. How to address the control design based on the triggering condition (27) is a challenging issue in event-triggered control; Event-triggered control and filtering should take network dynamics into account. In event-triggered control and

•

•

filtering, whether or not an event is triggered should be determined not only by the change of system measurements but also by the real-time dynamics of communication networks. If the current network traffic is busy, triggering an event is beneficial neither for the system performance nor for a guaranteed quality of service (QoS) of the network. If the current network traffic is idle, triggering events as many as possible in the sense that the good QoS of the network can be ensured may improve the system performance. Therefore, how to combine system measurements with network dynamics to define suitable triggering conditions in event-triggered control is of significance both in theory and in real applications, which is still challenging; In event-triggered control and filtering, it is often to assume that packet dropouts and packet disorders do not occur. This assumption is not practical when packets are transmitted through a communication network. How to deal with packet dropouts and packet disorders in eventtriggered control and filtering is challenging. Up to date, taking packet dropouts and packet disorders into account, no efficient approaches are proposed to design eventtriggered controllers and filters; and Distributed networked control and distributed networkbased filtering are still attractive and challenging. Although some results on these issues are reported in the literature, see the survey paper [95] and references therein, they are usually based on some strong assumptions when parts of network constraints are taken into account. In fact, many practical factors need to be considered for distributed control and filtering in network environments. To mention a few, network-induced delays in different channels between an agent and its neighbours are different and time-varying; packet dropouts occur randomly in different communication channels; the topologies of the agents in a distributed system are not always the same at any time; and so on. These factors indeed make the analysis and synthesis of distributed networked control and distributed network-based filtering more complicated, especially for distributed systems with a large number of agents. VI. C ONCLUSION

This paper has provided an overview on the theoretical development of networked control systems based on the results reported in the literature in the last decade. In-depth analysis and discussion has been made on sampled-data control, networked control and event-triggered control, where existing research approaches have been summarized. Moreover, advanced results on network-based filtering have been also reviewed both in the continuous-time domain and in the discretetime domain. Finally, some challenging problems have been presented to suggest the future research directions. R EFERENCES [1] K. Abidi and J. Xu, “Iterative learning control for sampled-data systems: From theory to practice,” IEEE Trans. Ind. Electron., vol. 58, no. 7, pp. 3002-3015, Jul. 2014.



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Xian-Ming Zhang received the M.S. degree in Applied Mathematics and the Ph.D. degree in Control Theory and Control Engineering from Central South University, Changsha, China, in 1991 and 2006, respectively. In April 1992, he joined Central South University, Changsha, China, where he was an Associate Professor within the School of Mathematics and Statistics. From February 2007 to December 2013, he was a Senior Postdoctoral Research Fellow within the Centre for Intelligent and Networked Systems, and a Lecturer within the School of Engineering and Technology, Central Queensland University, Rockhampton, Australia. In December 2014, he joined Griffith University, Gold Coast, Australia, where he is currently a Lecturer within the Griffith School of Engineering. He won the second National Natural Science Award in China in 2013, and won the first Hunan Provincial Natural Science Award in Hunan Province in China in 2011, both jointly with Professors Min Wu and Yong He. His current research interests include Hinfinity filtering, event-triggered control systems, networked control systems, neural networks, distributed systems, and time-delay systems.



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Qing-Long Han (M’09-SM’13) received the B.Sc. degree in Mathematics from Shandong Normal University, Jinan, China, in 1983, and the M.Sc. and Ph.D. degrees in Control Engineering and Electrical Engineering from East China University of Science and Technology, Shanghai, China, in 1992 and 1997, respectively. From September 1997 to December 1998, he was a Post-doctoral Researcher Fellow with the LAIIESIP (now renamed as LIAS-ENSIP), Universit´ e de Poitiers, France. From January 1999 to August 2001, he was a Research Assistant Professor with the Department of Mechanical and Industrial Engineering at Southern Illinois University at Edwardsville, USA. From September 2001 to December 2014, he was Laureate Professor, Associate Dean (Research and Innovation) with the Higher Education Division, and Founding Director of the Centre for Intelligent and Networked Systems at Central Queensland University, Australia. In December 2014, he joined Griffith University, where is currently Deputy Dean (Research) with the Griffith Sciences, and a Professor with the Griffith School of Engineering. In March 2010, he was appointed Chang Jiang (Yangtze River) Scholar Chair Professor by Ministry of Education, China. In August 2011, he was appointed 100 Talents Program Chair Professor by Shanxi Province of China. He is one of The World’s Most Influential Scientific Minds: 2014 and is a Highly Cited Researcher in the field of Engineering according to Thomson Reuters. Professor Han’s research interests include networked control systems, neural networks, time-delay systems, multi-agent systems and complex systems.

Xinghuo Yu (M’92-SM’98-F’08) received BEng and MEng degrees from the University of Science and Technology of China, Hefei, China, in 1982 and 1984, and PhD degree from Southeast University, Nanjing, China in 1988, respectively. He is currently Founding Director of the Platform Technologies Research Institure of RMIT University (Royal Melbourne Institute of Technology), Melbourne, Australia. Professor Yu’s research interests include variable structure and nonlinear control, complex and intelligent systems, and smart energy systems. He served/is serving as Associate Editor in four IEEE Transactions (Automatic Control, Industrial Electronics, Industrial Informatics, and Circuits and Systems - Part I). Professor Yu is President-Elect (2016-2017) of IEEE Industrial Electronics Society. He received a number of awards and honors for his contributions, including 2013 Dr.-Ing. Eugene Mittelmann Achievement Award of IEEE Industrial Electronics Society and 2012 IEEE Industrial Electronics Magazine Best Paper Award.
