Analysis and Synthesis of Networked Control Systems

With the emergence of high speed network technology that allows a cluster of devices to be linked together economically to form distributed networks which are capable of remote data transmission and data exchanges, distributed control systems based on networks are increasing rapidly in various applications([83, 84]). Due to the use of networks, the complexity and the cost of distributed control systems are reduced greatly and the maintenance of the systems becomes much easier ([229]). Because of these attractive benefits, many industrial companies and institutes have shown great interest in applying various networks to remote control systems and manufacturing automation. As a result of extensive research and development, several network protocols for industrial control have been released, such as industrial Ethernet, ControlNet, and DeviceNet.

Recently there have been many of interests in NCSs, that is, control systems closed via possibly shared communication links with delay/bandwidth constraints. Particularly, Internet based control systems allow remote monitoring and adjustment of plants over the Internet. 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 [56, 78], and references therein. The main advantages of NCSs are low cost, simple installation and maintenance, and potentially high reliability. As the structure of NCSs is different from that of traditional control systems, there exist various specific problems in NCSs, for example, quantization, network delay, loss of data packets, network security and safety [148]. In recent years, more and more network techniques have been applied to control systems [133], much attention has been paid to the study of control design and stability analysis of NCSs ([29, 131, 229, 221, 106, 122]). A state estimation problem involving bit-rate communication capacity constraints is studied in [119]. A current survey of the emerging field of NCSs is provided in [7] and Hespanha, et. al review several recent results on estimation, analysis, and controller synthesis for NCSs in [70]. In [206], a practical architecture and some algorithms for the networked data fusion system with packet losses and variable delays are given, in which optimal state estimates through network are presented. However, one important issue is not considered in [206], that is, the measurement or estimated states have to be quantized before they are sent through network.

In recent years, NCSs have been actively investigated ([229, 236, 221, 161, 107, 198, 170, 210, 230, 58, 193, 202, 219, 206]). Due to the limited transmission capacity of the network and some devices in the closed-loop systems, data transmitted in practical NCSs should be quantized before they are sent to the next network node. Therefore, to achieve better performance of the considered systems, the effect of data quantization on the system should be taken into consideration. Furthermore, the network can be viewed as a web of unreliable transmission paths. Some packets not only suffer transmission delay but, even worse, can be lost during transmission. Thus, how such packet dropouts affect the performance of a NCS is an issue that must be studied.

As we know, two quantizers are used frequently. The uniform quantizer is introduced in above chapter, as for logarithmic quantizer, wonderful results have been obtained. In [40], a logarithmic quantizer is firstly presented for stabilization of a linear discrete-time system. Ref. [51] shows an alternative proof for the optimal design and extends the results to quantized output feedback and quantized quadratic performance control using the sector bound method. Output feedback control of discrete-time linear systems using a finite-level quantizer is studied in [52]. A new approach based on sector bound method is used to analyze the stability of quantized feedback control systems in [237]. Remote control system affected by quantized signal is considered in [86]. Based on a quantization dependent Lyapunov function, the study on stability analysis of quantized feedback control system is given in [55]. The problems of discontinuous stabilization and robust stabilization of nonlinear systems are discussed in [21] and [139], respectively. Ref. [57] considers the problem of dynamic out-feedback stabilization of NCSs. In [66] and [67], the adaptive quantized control is considered and an adaptive feedback control law is given to ensure Lyapunov stable and x(k)→0 as k→ ∞. In [185], quantization and packet dropout are considered simultaneously, packet losses rate and unstable poles of the plant are considered to ensure different stability of the system, such as stochastically quadratically stable and mean square practically stable.

Data fusion, one of the key technologies of NCSs, has been served as an interesting benchmark in the past decades. The fusion center deals with the information from the local sensors to improve the performance of the system. It has been applied in both military and nonmilitary fields. Military applications include: automated target recognition, guidance for autonomous vehicles, remote sensing, battlefield surveillance, and automated threat recognition systems, such as identification-friend-foe-neutral (IFFN) systems. Nonmilitary applications include: monitoring of manufacturing processes, condition-based maintenance of complex machinery, robotics, and medical applications.

Most of the earlier works are based on the measurements observed by sensors with synchronous samples at the same sampling rate [10, 20, 23, 28, 39]. Only a few pieces of work deal with asynchronous multirate multisensor data fusion. Based on continuous time systems, Alouani with his group [4] and Bar-Shalom et al. [10, 8] present some effective algorithms for asynchronous multisensor systems. As far as the discrete time systems are concerned, the related researches include the approaches based on multiscale system theory [72, 11, 12, 195, 211, 226], the batch process methods [104], and the algorithms based on the designing of multirate filter banks [42], etc. In the literature listed above, the missing of observations is rarely concerned, which is inclined to encounter in many application fields including communication, navigation, etc. For filtering of incomplete measurements, there are some interesting results. Among these, the algorithms presented by the team of Wang are promising that it has proper computation complexity and can generate nearly optimal state estimate [194]. Based on a discrete-time linear dynamic system, Kalman filtering with intermittent observations is studied in [169], where the arrival of the observations is modeled as a random process, and the statistical convergence property of Kalman filter is given. The modified Riccati equation is studied by Boers and his group [15]. Some useful results are presented as far as a single sensor observing a single target which is described by a linear state space model is concerned. Kalman filtering with faded measurements is studied in [173]. By use of peak covariance as an estimate of filtering deterioration caused by packet losses, the stability of Kalman filtering with Markovian packet losses is studied in [81] based on a linear time-invariant system. Bar-Shalom studies the state estimation with out of sequence measurements based on a time-invariant dynamic system [8]. Xia, Shang, Chen and Liu study the networked data fusion with packet losses and variable delays, and an optimal state estimate is generated [206]. However, in all these interesting papers, multirate systems are not concerned. The multi-rate linear minimum mean squared error state estimation problem is solved by use of the lifting technique [99]. While, asynchronous sampling and data losses are not concerned in this reference. To sum up, there are few results on the fusion of multirate sensors that sample asynchronously with measurements randomly missing. This motivates us for the present study.

In recent years, networked control and data fusion technology have become popular control problems which have been extensively studied under various assumptions and scenarios [197, 235. 98, 145, 91, 2, 206]. There has been a growing interest in the design of controllers based on the network systems such as traffic, communication, aviation and spaceflight [236]. Particularly, the rapid rising of Internet makes Internet based control systems accomplish remote monitoring and adjustment over a long distance. This makes the control systems benefit from the ways of retrieving data and reacting to plant fluctuations from anywhere around the world at any time [34, 179, 129, 127]. In NCSs, the plant, controller, sensor, actuator and reference command are connected through a network. As the structure of NCSs is different from that of traditional control systems, there exist various specific problems in NCSs, for example, network delay, loss of data packets, network security and safety [213].

With the development of network technology, an increasing number of network technology has been applied to control systems. The networked control has became a new area in control systems. In this chapter, we will make full use of the network feature and propose a new networked predictive control scheme with the optimal estimation.

It is clear that fault diagnosis (including fault detection and isolation, FDI) has been becoming an important subject in modern control theory and practice. Since the beginning of 1970s, research in fault diagnosis has been gaining increasing consideration world-wide in both theory and application [134, 47]. This development was mainly stimulated by the trend of automation towards more complexity and the growing demand for higher availability and security of control systems. However, a strong impetus also comes from the side of modern control theory estimation and parameter identification that have been made feasible by the spectacular progresses of computer technology [26].

The research of fault detection (FD) stems from its practical application to a variety of industries such as aerospace, energy systems, and process control to name a few. The main function of a FD scheme is to detect a fault when it happens, which may then be acted on by sending alarm signals, taking protection measures, or reconfiguring a running control scheme [135, 176]. The observer based FD scheme is currently receiving much attention [227], Generally speaking, an observer based FD system consists of an observer based residual generator and a residual evaluator. It is the state of the art that problems related to the observer based FD system design are mainly addressed in the context of improving system robustness against unknown disturbances and simultaneously enhancing system sensitivity to faults. Study on solving such problems builds the recent research which focus on designing observer based FD systems [69, 82].