Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

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.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Event-triggered Learning

The efficient exchange of information is an essential aspect of intelligent collective behavior. Event-triggered control and estimation achieve some efficiency by replacing continuous data exchange between agents with intermittent, or event-triggered communication. Typically, model-based predictions are used at times of no data transmission, and updates are sent only when the prediction error grows too large. The effectiveness in reducing communication thus strongly depends on the quality of the prediction model. In this article, we propose event-triggered learning as a novel concept to reduce communication even further and to also adapt to changing dynamics. By monitoring the actual communication rate and comparing it to the one that is induced by the model, we detect a mismatch between model and reality and trigger model learning when needed. Specifically, for linear Gaussian dynamics, we derive different classes of learning triggers solely based on a statistical analysis of inter-communication times and formally prove their effectiveness with the aid of concentration inequalities

Data analytics for stochastic control and prognostics in cyber-physical systems

In this dissertation, several novel cyber fault diagnosis and prognosis and defense methodologies for cyber-physical systems have been proposed. First, a novel routing scheme for wireless mesh network is proposed. An effective capacity estimation for P2P and E2E path is designed to guarantee the vital transmission safety. This scheme can ensure a high quality of service (QoS) under imperfect network condition, even cyber attacks. Then, the imperfection, uncertainties, and dynamics in the cyberspace are considered both in system model and controller design. A PDF identifier is proposed to capture the time-varying delays and its distribution. With the modification of traditional stochastic optimal control using PDF of delays, the assumption of full knowledge of network imperfection in priori is relaxed. This proposed controller is considered a novel resilience control strategy for cyber fault diagnosis and prognosis. After that, we turn to the development of a general framework for cyber fault diagnosis and prognosis schemes for CPSs wherein the cyberspace performance affect the physical system and vice versa. A novel cyber fault diagnosis scheme is proposed. It is capable of detecting cyber fault by monitoring the probability of delays. Also, the isolation of cyber and physical system fault is achieved with cooperating with the traditional observer based physical system fault detection. Next, a novel cyber fault prognosis scheme, which can detect and estimate cyber fault and its negative effects on system performance ahead of time, is proposed. Moreover, soft and hard cyber faults are isolated depending on whether potential threats on system stability is predicted. Finally, one-class SVM is employed to classify healthy and erroneous delays. Then, another cyber fault prognosis based on OCSVM is proposed --Abstract, page iv

HMM-Based Characterization of Channel Behavior for Networked Control Systems

We study the problem of characterizing the behavior of lossy and data corrupting communication channels in a networked control setting, where the channel\u27s behavior exhibits temporal correlation. We propose a behavior characterization mechanism based on a hidden Markov model (HMM). The use of a HMM in this regard presents multiple challenges including dealing with incomplete observation sequences (due to data losses and corruptions) and the lack of a priori information about the model complexity (number of states in the model). We address the first challenges by using the plant state information and history of received/applied control inputs to fill in the gaps in the observation sequences, and by enhancing the HMM learning algorithm to deal with missing observations . Further, we adopt two model quality criteria for determining behavior model complexity. The contributions of this paper include: (1) an enhanced learning algorithm for refining the HMM model parameters to handle missing observations, and (2) simultaneous use of two well-defined model quality criteria to determine the model complexity. Simulation results demonstrate over 90\% accuracy in predicting the output of a channel at a given time step, when compared to a traditional HMM based model that requires complete knowledge of the model complexity and observation sequence

Robust H

This paper investigates the problem of robust H∞ fault detection for networked Markov jump systems with random time-delay which is introduced by the network. The random time-delay is modeled as a Markov process, and the networked Markov jump systems are modeled as control systems containing two Markov chains. The delay-dependent fault detection filter is constructed. Furthermore, the sufficient and necessary conditions which make the closed-loop system stochastically stable and achieve prescribed H∞ performance are derived. The method of calculating controller, fault detection filter gain matrices, and the minimal H∞ attenuation level is also obtained. Finally, one numerical example is used to illustrate the effectiveness of the proposed method

Observer-based fault detection of technical systems over networks

The introduction of networks into technical systems for facilitating remote data transmission, low complexity in wiring and easy diagnosis and maintenance, raises new challenges in fault detection (FD), such as how to handle network-induced time-varying transmission delays, packet dropouts, quantization errors and bit errors. These factors lead to increasing interest in developing new structures and design schemes for FD of technical systems over networks. In this thesis all network-induced effects are analyzed and modeled systematically at first. By observing the stochastic inheritance of networks, an FD framework of Markov jumping linear systems is presented as a basis for the later developments. Then two observer-based schemes for the purpose of FD over networks with guaranteed false alarm rate (FAR) are proposed: a remote FD system and an FD system of networked control systems (NCSs). The remote FD scheme is for detecting faults in technical systems at a remote site, where system measurements are transmitted via networks. In this scheme, the coding mechanism of communication channels is investigated from the view point of control engineering and new methods are developed for optimal residual generation and evaluation by considering network-induced data loss and corruption. A novel design scheme of FD system is also developed for NCSs, where the technical system is networked, i.e. controllers, actuators and sensors are connected with communication channels. In this scheme, network-induced transmission delays, packet dropouts, quantization errors are taken into account for the design of the optimal FD system. The linear matrix inequalities (LMIs) and convex optimization techniques are applied for assisting the design procedures. The developed schemes are tested with numerical examples and implemented in a three-tank system benchmark, and their superiority to existing solutions is demonstrated. Existing restrictions are overcome and new observer-based FD schemes over networks are introduced having the following characteristics: (1) the residual generators in both schemes are optimal in the sense of achieving the best trade-off between sensitivity to system faults and robustness against system disturbances and network-induced effects; (2) the proposed schemes can provide reliability information of rising fault alarms by analyzing the mean and variance of residual signals. Such information is very useful for practical applications in industries; (3) the design of residual generators and computation of thresholds can be efficiently solved by means of existing LMI-solvers

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area