Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 543725, 11 pages

http://dx.doi.org/10.1155/2015/543725

## Fuzzy-Logic-Based Control, Filtering, and Fault Detection for Networked Systems: A Survey

^{1}Shanghai Key Lab of Modern Optical System, Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China^{2}Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK^{3}Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia^{4}School of Information Science and Technology, Donghua University, Shanghai 200051, China^{5}Department of Automation, Tsinghua University, Beijing 100084, China^{6}College of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China^{7}Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150001, China^{8}Department of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, China

Received 29 January 2015; Accepted 28 April 2015

Academic Editor: Anna Pandolfi

Copyright © 2015 Yuqiang Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.

#### 1. Introduction

*Networked Control Systems*. In recent years, with the rapid development of the network technology and application, networked systems have received comprehensive attention from researchers and engineers, and plenty of results have been reported; see, for example, [1–3]. The networked control system (NCS) is an integration of control science, network technology, and computer science. A NSC features many advantages such as simple installation and maintenance, reduced weight and power requirements, reduction of installation and maintenance costs, and decentralization of function [4, 5]. In a typical NCS, sensors, plants, controllers, and actuators are connected through communication networks undertaking the main transmission task between system elements. Broadly speaking, when the control loop is closed via a communication channel, the controlled system can be labeled as a NCS [6]. Due to its practicability and validity, the NCS has found various practical applications including industrial automation, remote surgery, building automation, mobile sensor networks, robot, intelligent vehicle systems, advanced aircraft, and so forth [7–9]. However, the presence of the network also introduces some disadvantages, for instance, time-varying network-induced delays, quantization, aperiodic sampling, packet dropouts, and channel fading. These network-induced constraints might seriously deteriorate the performance of the systems and sometimes even lead to instability. Therefore, analysis and design of NCSs have gained increasing concern in the past years; see, for example, [10].

Roughly speaking, the NCSs can be classified into the following two types according to the existing infrastructure in practice, namely, fieldbus control systems (FCSs) and Industrial Ethernet (IE). A fieldbus-based NCS is a special system whose control loops are closed through a fieldbus. Due to their generality and efficiency, FCSs have been widely used in automation. Industrial control practice has demonstrated that the manufacturing and production have benefited a lot from FCSs in the past decades. With the advent of fieldbus technologies, the traditional control structure has been changed remarkably. By employing fieldbus technologies, the field equipment, field instruments, and measurement device can be linked into a local network system so as to render the real-world applications of NCSs possible. There are many kinds of FCSs; for example, an overview of the current state of these fieldbus networks has been presented in [11], where various advantages of FCSs over point-to-point connections have been given. For details of the FCSs, readers are referred to [12, 13].

The Industrial Ethernet is another important type of NCSs, which also plays an important role in the actual production and scientific research. Birth and development of IE provide practical basis for NCSs and back up the applications of remote monitoring and control, networked manufacturing, intelligent control, and so forth. Compared with the traditional networks, IE network equipping modern intelligent switching technologies owns a variety of advantages such as low cost, easy extensibility, and flexible and fast networking. On the other hand, the widespread use of the existing industry standards has significantly reduced the compatibility risks and network complexity of IE networks by replacing some proprietary standards with the common standards. Because of their real-time performance, high reliability, and openness, IE networks have been widely applied in industrial control and gained significant achievements; see, for example, [14, 15].

*Fuzzy Control Systems*. Since the introduction of fuzzy set theory by Zadeh [16–18], the fuzzy logic theory has been developed in a variety of directions. Now, the fuzzy logic theory has found widespread applications in the control engineering, signal and information processing, pattern recognition, expert system, decision making, and so on. As one of the most successful applications of fuzzy sets and fuzzy logic, the research on fuzzy control problems has undergone great progress in the past decades [19]. Since the fuzzy-logic-based control was first used in the controller design for the steam engine in 1974 [20, 21], it has been extensively applied to system modeling, intelligence control, system identification, pattern recognition and classification, neural network, and so forth. Compared with conventional crisp control, fuzzy-logic-based control can model humans’ experience more accurately in a linguistic manner, thereby providing an efficient way to realize the intelligent control in industrial application.

In general, the fuzzy control system can be classified into two types: the model-free and the model-based fuzzy control system. The model-free fuzzy control scheme has witnessed great developments in recent years because, under certain circumstances, it outperforms other conventional model-free approaches such as the nonlinear adaptive or PID control. A large number of important results have appeared on this issue; see, for example, [22, 23]. The model-based fuzzy control systems include three types: type-1, type-2, and type-3 [24]. Presentation of these three different types of fuzzy control systems can be found in [25], from which one can know that type-2 fuzzy control system is actually a special case of type-1. There are many applications of type-1 fuzzy control systems; see, for example, [26–29]. The type-2 fuzzy control system has been widely applied in practice; see, for example, [30–34]. In fact, it was first proposed by Sugeno in [25] and first used for automobile tracking control [35]. The type-3 fuzzy control system is actually the T-S fuzzy model, which has proven to be an extremely effective method for tackling nonlinear NCSs. As pointed out in [36], the Takagi-Sugeno (T-S) model is able to approximate any smooth nonlinear function to any degree of accuracy in any convex compact region. By using a set of local linear models which are smoothly connected by nonlinear fuzzy membership functions to present a nonlinear plant, the T-S fuzzy model has brought the analysis and synthesis of nonlinear systems into a unified framework. Through decades of developments, the T-S fuzzy model has enjoyed an extensive utilization; see, for example, [37–40] for some original literature and [41, 42] for the latest literature.

*Organization of This Paper*. The main objective of this paper is to provide a general overview on the advance of fuzzy-logic-based methodology in networked systems. A brief description of characteristics and advantages of the networked systems is given, where two types of practically used networked systems are introduced. Some latest literature relating to network-induced phenomena of NCSs has been presented. Advances of fuzzy-logic-based control, filtering, and fault detection for networked systems are summarized, and the recent progresses of applications and improvements of the fuzzy-logic-based control in the NCSs have been reviewed.

The remainder of this paper is organized as follows. In Section 2, network-induced phenomena such as packet dropouts, time delays, signal quantization, and link failures are discussed. Section 3 introduces latest results on fuzzy-logic-based control for NCSs with emphasis on three common control schemes, namely, PID control, adaptive control, and tracking control. In Section 4, the fuzzy-logic-based filtering and fault detection problems are examined and the corresponding results are presented. In Section 5, some concluding remarks of this paper are drawn and several possible directions for further research are pointed out.

#### 2. Network-Induced Phenomena

Network-induced phenomena emerge for mainly two reasons. For one thing, because the network itself is a nonlinear dynamic system, its Quality-of-Service depends on many conditions such as cable quality, bandwidth capacity, and link quality. Therefore, any variation (e.g., cable aging, interface failures, limited bandwidth, and network congestion) would affect the application service of networked systems. For another thing, the risk and frequency of the occurrence of faults/failures in networked systems are higher than those for traditional systems due to the inherent peculiarity of the NCSs such as multiple components, large scale, wide distribution, and high complexity. Moreover, the data in the open condition of network transferring is vulnerable to malicious attacks and theft. Therefore, it is very necessary to address the data security of NCS. For these reasons, the network-induced constraints of NCSs have acquired unprecedented interests in the recent years. In this section, the network-induced phenomena will be discussed, which include packet dropouts, time delays, signal quantizations, actuator/sensor/link failures channel fading, and data security.

As an important network-induced constraint, the* packet dropout* is an inevitable problem in NCSs due to the channel noise and the network congestion. It is generally known that packet dropouts have significant impacts on the analysis and synthesis of NCSs. If it is not properly handled, the packet dropout would degrade the NCS performance and even destabilize systems. On the other hand, the* communication time delay* is a well-known kind of network-induced phenomena that exist universally in the networked systems. According to the way the delays occur, there are some different types of delays including constant delays, time-varying delays, discrete delays, and distributed delays. In the past few decades, the topics of research on time-delay systems have been attracting constant attention from the control community. Because of the universality and complexity of the time delays, it is very important and challenging to design effective algorithms so as to reduce the impact from delays on the NCSs. Generally speaking, the existing stability criteria for time-delay systems can be classified into two types, that is, the delay-independent conditions and the delay-dependent ones; see, for example, [43, 44]. So far, various research methodologies have been developed for studying the time delays and some recent results can be found in [45, 46].

In networked systems, it is generally known that the signals are usually quantized before communication. Due to either rounding or truncation of quantization, the* quantization effect* occurs frequently which poses great challenges on the design of controllers/filters for NCSs. Obviously, the control algorithm specifically designed for a system without quantization will fail to work when the quantization effects do arise. Although the quantization error can be reduced by increasing the number of quantized bits, it may lead to increased power consumption which, in turn, hinder the development of the NCSs [47]. For purpose of reducing energy consumption in NCSs, some power scheduling strategies for quantization have been proposed; see, for example, [47–49]. On the other hand, since the quantization is a critical factor affecting the stability and performance of NCSs, it has become an important issue for researchers to work on control system design with quantization effects; for example, some quantized state feedback control schemes have been employed to solve the stabilization problems in NCSs; see, for example, [50–52].

On another research front of reliable control, the failures in NCS can occur within three main categories, namely, the system devices, sensor nodes, and communication components due primarily to the malfunction such as node crash, low battery power, system reboot, and routing loops. Different from the malfunction or performance failures, the actuator/sensor/link failures often lead to serious and even disastrous outcomes. Therefore, it is of practical significance to look at how to improve the stability, reliability, and intelligence of NCSs with possible failures under different circumstances. In fact, the fault detection, fault diagnosis, and fault tolerant control problems have been widely investigated in the areas especially for NCSs; see, for example, [53–55].

As a common kind of network-induced phenomena, the channel fading has received initial research attention in the context of filter/control designs of NCSs in recent years. Because the wireless communication links are especially susceptible to the fading effects, the channel fading constitutes one of the most dominant features of wireless NCSs. Generally speaking, the channel fading can fall into two major categories, namely, the multipath induced fading and the shadow fading [56]. Since the signal transmitted by a fading channel can be seriously corrupted, design of a reliable controller and filter which is insensitive to fading influence becomes urgent in NCSs. Fortunately, however, some preliminary works have appeared; see, for example, [57, 58].

In the traditional NCSs, the security protection has not been considered seriously, which brings a series of data security problems such as the illegal invasion, information theft, and data tampering in an unencrypted communication condition. If the NCSs are maliciously intruded, the target physical system may be uncontrolled. For these reasons, the data and communication protection of NCSs has stimulated the interests of people in very recent years. Based on the coevolutionary algorithms, the tradeoff problem between the security and the performance of NCSs has been discussed in [59]. With the help of the stochastic game theory, an optimal defense mechanism for the NCSs under jamming attacks has been presented in [60], where the dynamic interactions between the attacker and the defender in NCSs was modeled by a two-player zero-sum stochastic game. In [61], the security decisions has been considered for the identical plant-controller systems controlled over a shared communication network.

#### 3. Fuzzy-Logic-Based Control of NCSs

Compared with traditional control methods, the fuzzy-logic-based control scheme poses the feature of intelligence. In fact, the fuzzy-logic-based control is an important branch of intelligent control, which plays a significant role in enhancing the intelligent level of control systems. In order to obtain better control performance and intelligence, fuzzy-logic-based control has been gradually utilized to improve the traditional control scheme in recent years. In this section, we will provide an overview on the integrations of fuzzy control and some traditional control methods in the NCSs, such as fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control. The general frame of fuzzy-logic-based control in NCSs is shown in Figure 1.