**7. Networked control systems**

Traditionally, the different components (i.e., sensor, controller, and actuator) in a control system are connected via wired, point-to-point links, and the control laws are designed and operate based on local continuously-sampled process output measurements.

In recent years, there has been a growing interest in the design of controllers based on the network systems in several areas such as traffic, communication, aviation and spaceflight [86]. The networked control systems (NCSs) is defined as a feedback control system where control loops are closed through a real-time network [96, 97], which is different from tradi‐ tional control systems. For an overview, the readers can refer to [97], which systematically addresses several key issues (band-limited channels, sampling and delay, packet loss, sys‐ tem architecture) that make NCSs distinct from other control systems.

#### **7.1. Characteristics of NCSs**

**Advantages.** Communication networks make the transmission of data much easier and pro‐ vide a higher degree of freedom in the configuration of control systems. Network-based communication allows for easy modification of the control strategy by rerouting signals, having redundant systems that can be activated automatically when component failure oc‐ curs. Particularly, NCSs allow remote monitoring and adjustment of plants over the Inter‐ net. This enables the control system to benefit from the way it retrieves data and reacts to plant fluctuations from anywhere around the world at any time, see for example, [98-101] and references therein.

**Disadvantages.** Although the network makes it convenient to control large distributed sys‐ tems, new issues arise in the design of a NCSs. Augmenting existing control networks with real-time wired or wireless sensor and actuator networks challenges many of the assump‐ tions made in the development of traditional process control methods dealing with dynami‐ cal systems linked through ideal channels with flawless, continuous communication. In the context of networked control systems, key issues that need to be carefully handled at the control system design level include data losses due to field interference and time-delays due to network traffic as well as due to the potentially heterogeneous nature of the additional measurements (for example, continuous, asynchronous and delayed) [102]. These issues will deteriorate the performance and may even cause the system to be unstable.

Hence, the main question is how to design the NCSs include the handling of data losses, time-varying delays, and the utilization of heterogeneous measurements to maintain the closed-loop stability while improving the closed-loop performance.

eled as a discrete-time switched linear system with arbitrary switching was studied. In [127], (see also [128-130]), optimal control of linear time-invariant systems over unreliable commu‐ nication links under different communication protocols (with and without acknowledgment of successful communication) was investigated and sufficient conditions for the existence of

Discrete-Time Model Predictive Control http://dx.doi.org/10.5772/51122 99

Although, within control theory, the study of control over networks has attracted considera‐ ble attention in the literature, most of the available results deal with linear systems (e.g.,

MPC framework is particularly appropriate for controlling systems subject to data losses be‐ cause the actuator can profit from the predicted evolution of the system. In this section, re‐

Several methodologies have been reported in the open literature to handle with the prob‐ lems mentioned above in networked systems. Among these papers, two basic control strat‐ egies are applied when the packet dropping happens, they are zero-input schemes, by which the actuator input is set to zero when the control packet is lost, and hold-input scheme which implies the previous control input is used again when the control packet drops. The further

The work of [133] presents a novel control technique combining modified MPC and modi‐ fied Smith predictor to guarantee the stability of NCSs. Especially, the key point in this paper is that the future control sequence is used to compensate for the forward communi‐ cation time delay and predictor is responsible for compensating the time delay in the back‐

Although much research work have been done in NCSs, many of those results simply treat the NCSs as a system with time delay, which ignores NCSs features, e.g., random network delay and data transmission in packets [134]. In order to solve the problem, Markovian jump system can be used to model the random time-delay. Moreover, most work have also ignor‐ ed another important feature of NCSs. This feature is that the communication networks can transmit a packet of data at the same time, which is not done in traditional control sys‐ tems. We have proposed a new networked control scheme – networked predictive control which mainly consists of a control prediction generator and a network dropout/delay com‐ pensator. It is first assumed that control predictions based on received data are packed and sent to the plant side through a network. Then the network dropout/delay compensator chooses the latest control value from the control prediction sequences available on the plant side, which can compensate for the time delay and data dropouts. The structure of the networked predictive control system (NPCS) is shown as Figure 4. The random network delay in the forward channel in NCS has been studied in [135]. Some other results has been obtained in [136] and [137], where the network-induced delay is not in the form of a Mar‐

research is proposed in [132] by directly comparing the two control methods.

stabilizing control laws were derived.

sults from our works are summarized.

[100, 131]).

**7.3. Our works**

ward channel.

kov chain.

#### **7.2. Results on NCSs**

To solve these problems, various methods have been developed, e.g., augmented determin‐ istic discrete-time model, queuing, optimal stochastic control, perturbation, sampling time scheduling, robust control, fuzzy logic modulation, event-based control, end-user control adaptation, data packet dropout analysis, and hybrid systems stability analysis. However, these methods have put some strict assumptions on NCSs, e.g., the network time delay is less than the sampling period [109, 110]. The work of [111] presents an approach for stability analysis of NCSs that decouples the scheduling protocol from properties of the network-free nominal closed-loop system. The problem of the design of robust *H∞*controllers for uncer‐ tain NCSs with the effects of both the network-induced delay and data dropout has been considered in [112] the network-induced time delay is larger than one sampling period, but there is no compensation for the time delay and data dropout.

A common approach is to insert network behavior between the nodes of a conventional con‐ trol loop, designed without taking the network behavior into account. More specifically, in [114], it was proposed to first design the controller using established techniques considering the network transparent, and then to analyze the effect of the network on closed-loop sys‐ tem stability and performance. This approach was further developed in [115] using a small gain analysis approach.

In the last few years, however, several research papers have studied control using the *IEEE*802.11 and Bluetooth wireless networks, see, for example, [116-119] and the references therein. In the design and analysis of networked control systems, the most frequently stud‐ ied problem considers control over a network having constant or time-varying delays. This network behavior is typical of communications over the Internet but does not necessarily represent the behavior of dedicated wireless networks in which the sensor, controller, and actuator nodes communicate directly with one another but might experience data losses. An appropriate framework to model lost data, is the use of asynchronous systems [120-122] and the process is considered to operate in an open-loop fashion when data is lost.

The most destabilizing cause of packet loss is due to bursts of poor network performance in which case large groups of packets are lost nearly consecutively. A more detailed descrip‐ tion of bursty network performance using a two-state Markov chain was considered in [123]. Modeling networks, using Markov chains results in describing the overall closed-loop system as a stochastic hybrid system [120]. Stability results have been presented for particu‐ lar cases of stochastic hybrid systems (e.g., [124, 125]). However, these results do not directly address the problem of augmentation of dedicated, wired control systems with networked actuator and sensor devices to improve closed-loop performance.

With respect to other results on networked control, in [126], stability and disturbance at‐ tenuation issues for a class of linear networked control systems subject to data losses mod‐ eled as a discrete-time switched linear system with arbitrary switching was studied. In [127], (see also [128-130]), optimal control of linear time-invariant systems over unreliable commu‐ nication links under different communication protocols (with and without acknowledgment of successful communication) was investigated and sufficient conditions for the existence of stabilizing control laws were derived.

Although, within control theory, the study of control over networks has attracted considera‐ ble attention in the literature, most of the available results deal with linear systems (e.g., [100, 131]).
