Performance-Based Hierarchical Fault-Tolerant Control for Closed-Loop Systems With Multiplicative Faults: A Data-Driven Design Method.

Fault-tolerant control (FTC) is vital for the safety and reliability of automatic systems. Most of the existing FTC methods are developed for open-loop systems subject to additive faults, regardless of the widely present control loops and multiplicative faults within systems. In this article, a performance-based FTC strategy is proposed for the closed-loop systems with multiplicative faults. Considering the high efforts in modeling complex systems, the proposed FTC strategy is realized in the data-driven context. Specifically, a nominal feedback-feedforward controller is first established for the fault-free systems. By selecting the system stability and reference tracking behavior as the key performance indices, two performance evaluators are constructed to detect and classify the occurred multiplicative faults based on the fault-induced effects on the system performance. Then, with the aid of the coprime factorization technique, the multiplicative faults, in the form of additive perturbations to the system coprime factors, are estimated utilizing the closed-loop process data. Furthermore, based on the fault knowledge, a hierarchical fault-tolerant tracking controller is developed according to the levels of system performance degradations, where the functional controller parameters are reconfigured with different priorities. Finally, case studies are provided to validate the effectiveness of the proposed method.

A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

This article aims to analyze H∞ stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

Important-Data-Based DoS Attack Mechanism and Resilient H∞ Filter Design for Networked T-S Fuzzy Systems.

This article is concerned with the security problems for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different from most existing DoS attack models, the proposed attack mechanism can utilize the information of packets, evaluate the importance degree of packets, and only attack the most "important" ones. As such, a larger system performance degradation can be expected. Second, corresponding to the proposed IDB DoS mechanism, a resilient H∞ fuzzy filter is designed from the defender's point of view to alleviate the negative effect of the attack. Furthermore, since the defender does not know the attack parameter, an algorithm is designed to estimate it. In a word, a unified attack-defense framework is developed in this article for networked T-S fuzzy systems with asynchronous premise constraints. With the help of the Lyapunov functional method, sufficient conditions are successfully established to compute the desired filtering gains and ensure the H∞ performance of the filtering error system. Finally, two examples are exploited to demonstrate the destructiveness of the proposed IDB DoS attack and the usefulness of the developed resilient H∞ filter.

Quasi-Consensus Control for Stochastic Multiagent Systems: When Energy Harvesting Constraints Meet Multimodal FDI Attacks.

In this article, the quasi-consensus control problem is investigated for a class of stochastic nonlinear time-varying multiagent systems (MASs). The innovation points of this research can be highlighted as follows: first of all, the dynamics of the plant are stochastic, nonlinear, and time varying, which resembles the natural systems in practice closely. Meanwhile, an energy harvesting protocol is put forward to collect adequate energy from the external environment. Second, as a generalization of the existing result, the ultimate control objective is quasi-consensus in a probabilistic sense, that is, designing a distributed control protocol in order that the probability of centering the allowable region for the states of each agent is larger than some predetermined values. Third, the MASs are subject to false data-injection (FDI) attacks, and a more general multimodal FDI model is proposed. On the basis of the probabilistic-constrained analysis technique and the recursive linear matrix inequalities (RLMIs), sufficient conditions are provided to guarantee the probabilistic quasi-consensus property. To derive the controller gains, an optimal probabilistic-constrained algorithm is designed by solving a convex optimization problem. Finally, two examples are provided to substantiate the validity of the proposed framework.

Chance-Constrained H∞ State Estimation for Recursive Neural Networks Under Deception Attacks and Energy Constraints: The Finite-Horizon Case.

In this article, the chance-constrained H∞ state estimation problem is investigated for a class of time-varying neural networks subject to measurements degradation and randomly occurring deception attacks. A novel energy-constrained deception attack model is proposed, in which both the occurrence of the attack and the selection of released faked packet are random and the energy of the deception attack is introduced, calculated, and analyzed quantitatively. The main purpose of the addressed problem is to design an H∞ estimator such that the prefixed probabilistic constraints of the system error dynamics are satisfied and the H∞ performance is also ensured. Subsequently, the explicit expression of the estimator gains is derived by solving a minimization problem subjected to certain recursive inequality constraints. Finally, a numerical example and a practical three-tank system are utilized to demonstrate the correctness and effectiveness of the proposed estimation scheme.

Event-based fuzzy resilient control of nonlinear DC Microgrids under denial-of-service attacks.

This paper focuses on designing an event-triggered fuzzy resilient controller for networked nonlinear DC microgrid (MG) with constant power loads (CPLs) in the presence of denial-of-service (DoS) attacks. First, an attack-resilient event-triggered communication scheme is introduced to reduce the communication overhead of the DC MG while achieving the desired performance despite the presence of the DoS attacks. Second, the nonlinear event-triggered DC MG system with CPLs is modeled as a T-S fuzzy system with artificial delay through the sector nonlinearity approach combined with time-delay system modeling method. Then, by employing the noncontinuous piecewise Lyapunov-Krasovskii functional (NPLKF) approach, the asymptotic stability criterion of the built event-triggered T-S fuzzy DC MG is obtained in the form of linear matrix inequalities (LMIs), and a design method of fuzzy controller is proposed. Finally, case studies are presented to validate the efficiency of our proposed method.

Histological image color normalization using a skewed normal distribution mixed model.

Color variation between histological images may influence the performance of computer-aided histological image analysis. Therefore, among the most essential and challenging tasks in histological image analysis are the reduction of the color variation between images and the preservation of the histological information contained in the images. In recent years, many methods have been introduced with respect to the color normalization of histological images. In this study, we introduce a new clustering method referred to as the skewed normal distribution mixed model clustering algorithm. Realizing that the color distribution of hue values approximates the combination of several skewed normal distributions, we propose to use the skewed normal distribution mixture model to analyze the hue distribution. The proposed skewed normal distribution mixture model clustering algorithm includes saturation-weighted hue histograms because it takes into account the saturation and hue information of a particular histogram image, which can diminish the influence of achromatic pixels. Finally, we conducted extensive experiments based on three data sets and compared them with commonly used color normalization methods. The experiments show that the proposed algorithm has better performance in stain separation and color normalization compared to other methods.

Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach.

The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based H∞ controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.

Dynamic Event-Triggered Output Feedback Control for Networked Systems Subject to Multiple Cyber Attacks.

This article is concerned with the problem of the H∞ output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed H∞ performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.

Co-Design of Dynamic Event-Triggered Communication Scheme and Resilient Observer-Based Control Under Aperiodic DoS Attacks.

This article is concerned with the problem of observer-based dynamic event-triggered control for a networked control system (NCS) under a class of power-constrained denial-of-service (DoS) attacks that aim at impeding the network communication from time to time. First, by carefully modeling such DoS attacks as aperiodic pulse-width-modulated (PWM) jamming signals, a switching observer, adapting to the DoS attacks, is delicately constructed to deal with the unavailability of full-state information. Second, to economize the limited bandwidth resources, a dynamic event-triggered communication scheme is designed under the aperiodic DoS jamming attacks, whose duration and frequency are assumed to be restricted. Third, a switching system model with artificial state delay is formulated, which characterizes the effects of the aperiodic DoS attacks and event-triggered communication scheme in a unified framework. Then, the asymptotic stability analysis and controller/observer synthesis conditions of the resulting switching system are obtained by using a piecewise Lyapunov-Krasovskii functional approach. Furthermore, a co-design method of the dynamic triggering parameters, controller, and observer gains is presented. Finally, an example is employed to verify the effectiveness of the obtained results.

Memory-Based Event-Triggering H∞ Load Frequency Control for Power Systems Under Deception Attacks.

This article proposes a memory-based event-triggering H∞ load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory H∞ LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

Finite-horizon tracking control for a class of stochastic systems subject to input constraints and hybrid cyber attacks.

This paper is concerned with the probabilistic-constrained finite-horizon tracking control problem for a class of stochastic systems subject to randomly occurring hybrid cyber attacks and input constraints. Both the randomly occurring denial-of-service (DOS) attacks and randomly occurring deception attacks are considered in an unified framework. The purpose of the current study is to design an observer-based tracking controller such that: over a finite horizon, (1) the variance of the estimation error is less than certain bound at each time step, (2) the probability of the tracking error falling in certain region should larger than a specified value and the region is minimized at each time step. To achieve those purposes, an improved multi-dimensional Chebyshev inequality method is first utilized to convert the probabilistic constraint to a deterministic one. Then an observer-based tracking control method is designed to estimate the state and design the tracking controller, which are realized through solving a set of recursive linear matrix inequalities. By using the proposed algorithm, the observer and tracking controller gains can be solved out in terms of the solution to a convex optimization problem. A simulation example is finally given to demonstrate the effectiveness and applicability of the proposed method.

Event-based finite-time state estimation for Markovian jump systems with quantizations and randomly occurring nonlinear perturbations.

This paper is concerned with finite-time state estimation for Markovian jump systems with quantizations and randomly occurring nonlinearities under event-triggered scheme. The event triggered scheme and the quantization effects are used to reduce the data transmission and ease the network bandwidth burden. The randomly occurring nonlinearities are taken into account, which are governed by a Bernoulli distributed stochastic sequence. Based on stochastic analysis and linear matrix inequality techniques, sufficient conditions of stochastic finite-time boundedness and stochastic H∞ finite-time boundedness are firstly derived for the existence of the desired estimator. Then, the explicit expression of the gain of the desired estimator are developed in terms of a set of linear matrix inequalities. Finally, a numerical example is employed to demonstrate the usefulness of the theoretical results.

Event-triggered reliable H∞ filter design for networked systems with multiple sensor distortions: A probabilistic partition approach.

In this paper, the problem of event-triggered reliable H∞ filtering for networked systems with multiple sensor distortions is investigated. The interval of sensor distortion in each channel is partitioned several segments. By introducing a set of rand variables, the model of multiple sensor distortions with their information of probability distribution in each segment is established, which is more general than the one in open results. Furthermore, an event-triggered communication scheme is proposed to mitigate the utility of limited network bandwidth. Then a unified model with consideration of the event-triggered communication scheme and the sensor distortion is put forward. Based on this model, sufficient conditions of the mean square stability of the filtering error system and H∞ filter parameters are achieved. Finally, a simulation example is exploited to demonstrate the effectiveness of the presented method.

Delay-distribution-dependent H∞ state estimation for delayed neural networks with (x,v)-dependent noises and fading channels.

This paper deals with the H∞ state estimation problem for a class of discrete-time neural networks with stochastic delays subject to state- and disturbance-dependent noises (also called (x,v)-dependent noises) and fading channels. The time-varying stochastic delay takes values on certain intervals with known probability distributions. The system measurement is transmitted through fading channels described by the Rice fading model. The aim of the addressed problem is to design a state estimator such that the estimation performance is guaranteed in the mean-square sense against admissible stochastic time-delays, stochastic noises as well as stochastic fading signals. By employing the stochastic analysis approach combined with the Kronecker product, several delay-distribution-dependent conditions are derived to ensure that the error dynamics of the neuron states is stochastically stable with prescribed H∞ performance. Finally, a numerical example is provided to illustrate the effectiveness of the obtained results.

Delay-distribution-dependent stability and stabilization of T-S fuzzy systems with probabilistic interval delay.

In this paper, we are concerned with the problem of stability analysis and stabilization control design for Takagi-Sugeno (T-S) fuzzy systems with probabilistic interval delay. By employing the information of probability distribution of the time delay, the original system is transformed into a T-S fuzzy model with stochastic parameter matrices. Based on the new type of T-S fuzzy model, the delay-distribution-dependent criteria for the mean-square exponential stability of the considered systems are derived by using the Lyapunov-Krasovskii functional method, parallel distributed compensation approach, and the convexity of some matrix equations. The solvability of the derived criteria depends not only on the size of the delay but also on the probability distribution of the delay taking values in some intervals. The revisions of the main criteria in this paper can also be used to deal with the case when only the information of variation range of the delay is considered. It is shown by practical examples that our method can lead to very less conservative results than those by other existing methods.