**7.3. Our works**

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

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

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

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 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

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‐

the process is considered to operate in an open-loop fashion when data is lost.

actuator and sensor devices to improve closed-loop performance.

closed-loop stability while improving the closed-loop performance.

there is no compensation for the time delay and data dropout.

**7.2. Results on NCSs**

98 Advances in Discrete Time Systems

gain analysis approach.

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‐ sults from our works are summarized.

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

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‐ ward channel.

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‐ kov chain.

dedicated local control networks can be augmented with additional networked (wired and/or wireless) actuator/sensor devices have become cheap and easy-to-install [141, 142]. These large scale systems are composed by many physically or geographically divided sub‐ systems. Each subsystem interacts with some so called neighbouring subsystems by their states and their inputs. The technical target is to achieve some global performance of entire system (or a common goal of all subsystems). Actually, it is difficult to control with a cen‐ tralized control structure due to the required inherent computational complexity, robustness

Discrete-Time Model Predictive Control http://dx.doi.org/10.5772/51122 101

For all these reasons, many distributed control structures have been developed and applied

**About MPC.** The aim of this section is to review the distributed control approaches adopt‐ ed, and to provide a wide list of references focusing the attention on the methods based on MPC. This choice is motivated by the ever increasing popularity of MPC in the process in‐ dustry, see e.g. the survey papers [143, 144] on the industrial applications of linear and non‐ linear MPC. Moreover, in recent years many MPC algorithms have been developed to guarantee some fundamental properties, such as the stability of the resulting closed-loop system or its robustness with respect to a wide class of external disturbances and/or model uncertainties, see e.g. the survey paper [2]. Especially, MPC is also a natural control frame‐ work to deal with the design of coordinated, distributed control systems because of its abili‐ ty to handle input and state constraints, and also because it can account for the actions of other actuators in computing the control action of a given set of control actuators in realtime. Therefore, MPC is now recognized as a very powerful approach with well established theoretical foundations and proven capability to handle the problems of large scale systems.

1) *Centralized Control.* MPC is normally implemented in a centralized fashion. One controller is able to acquire the information of the global system, computes all the control inputs for the system, and could obtain a good global performance. In large-scale interconnected sys‐ tems, such as power systems, water distribution systems, traffic systems, etc., such a central‐ ized control scheme may not suitable or even possible apply to large scale system for some reasons: (1) there are hundreds of inputs and outputs. It requires a large computational ef‐ forts in online implementation (2) when the centralized controller fails, the entire system is out of control and the control integrity cannot be guaranteed when a control component fails (3) in some cases, e.g. in multi-intelligent vehicle system, the global information is un‐ available to each controller and (4) objections to centralized control are often not computa‐ tional, however, but organizational. All subsystems rely upon the central agent, making plantwide control difficult to coordinate and maintain. These obstacles deter implementa‐

In recent years, there is a trend for the development of decentralized and distributed MPC

due to the disadvantages of centralized MPC mentioned above (e.g.,[145, 146]).

and reliability problems and communication bandwidth limitations.

over the recent decades.

**Other control structures**

tion of centralized control for large-scale plants.

**8.2. The reasons why DMPC is adopted**

**Figure 4.** The networked predictive control system.

But, the random network delay in the forward and feedback channels makes the control de‐ sign and stability analysis much more difficult. In [100] proposes a predictive control scheme for NCS with random network delay in both the feedback and forward channels and also provides an analytical stability criteria for closed-loop networked predictive control (NPC) systems. Furthermore, [138] can overcome the effects caused by both the unknown network delay and data dropout. Recently, [139] mainly focus on the random transmission data dropout existing in both feedback and forward channels in NCSs. So, the network-in‐ duced time delay is not discussed here.

In fact, using the networked predictive control scheme presented in this section, the control performance of the closed-loop system with data dropout is very similar to the one without data dropout.
