State-Saturated Recursive Filter Design for Stochastic Time-Varying Nonlinear Complex Networks Under Deception Attacks

Optimized Distributed Filtering for Saturated Systems With Amplify-and-Forward Relays Over Sensor Networks: A Dynamic Event-Triggered Approach

In this article, the optimized distributed filtering problem is studied for a class of saturated systems with amplify-and-forward (AF) relays via a dynamic event-triggered mechanism (DETM). The AF relays are located in the channels between sensors and filters to prolong the transmission distance of signals, where the transmission powers of sensors and relays can be described by a sequence of random variables with a known probability distribution. With the purpose of alleviating the communication burden and preventing data collision, the DETM is used to schedule the transmission cases of nodes by dynamically adjusting the triggered threshold according to the practical requirements. An upper bound matrix (UBM) of the filtering error (FE) covariance is first provided under the sense of variance constraint and the proper filter gain is further constructed via minimizing the proposed UBM. In addition, the boundedness evaluation regarding the trace of the UBM is provided. Finally, simulation experiments are used to illustrate the usefulness of the developed distributed recursive filtering scheme.

Resilient event-triggering adaptive neural network control for networked systems under mixed cyber attacks

This paper addresses the resilient event-triggering adaptive neural network (NN) control problem for networked control systems under mixed cyber attacks. Compared with the conventional event-triggered mechanism (ETM) with constant threshold, a novel resilient ETM is designed to withstand the affect of denial-of-service attacks and conserve communication resources. Different from the energy-bounded deception attacks, an unknown state-dependent nonlinear attack signal is considered in this work. To identify the deception attack, the NN technique is utilized to approximate the unknown attack signal. Subsequently, an adaptive controller is established to compensate for the malicious affects of deception attacks on the system. Furthermore, sufficient conditions for the boundedness of the system are derived via applying the Lyapunov functional, and a co-design strategy for control gain and event-triggering parameter is provided. Finally, the feasibility of the proposed approach is validated through a robot manipulator system.

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.

Adaptive NN Fixed-Time Fault-Tolerant Control for Uncertain Stochastic System With Deferred Output Constraint via Self-Triggered Mechanism

For a class of nonstrict-feedback stochastic nonlinear systems with the injection and deception attacks, this article explores the problem of adaptive neural network (NN) fixed-time control ground on the self-triggered mechanism in a pioneering way. After developing the self-triggered mechanism and the delay-error-dependence function, a neural adaptive delay-constrained fault-tolerant controller is proposed by employing the backstepping technique. The self-triggered mechanism does not require an additional observer to determine the time of the data transmission, which reduces the consumption of the system resources more efficiently. In addition, the whole Lyapunov function with the delay-error-dependence term is developed to solve the deferred output constraint problem. Under the proposed controller, it can be proven that all the signals within the closed-loop system are semiglobally uniformly bounded in probability, while the convergence time is independent of the initial state and the deferred output constraint control performance is achieved. The feasibility and the superiority of the proposed control strategy are shown by some simulations.

Cluster Synchronization Control for Discrete-Time Complex Dynamical Networks: When Data Transmission Meets Constrained Bit Rate

In this article, the cluster synchronization control problem is studied for discrete-time complex dynamical networks when the data transmission is subject to constrained bit rate. A bit-rate model is presented to quantify the limited network bandwidth, and the effects from the constrained bit rate onto the control performance of the cluster synchronization are evaluated. A sufficient condition is first proposed to guarantee the ultimate boundedness of the error dynamics of the cluster synchronization, and then, a bit-rate condition is established to reveal the fundamental relationship between the bit rate and the certain performance index of the cluster synchronization. Subsequently, two optimization problems are formulated to design the desired synchronization controllers with aim to achieve two distinct synchronization performance indices. The codesign issue for the bit-rate allocation protocol and the controller gains is further discussed to reduce the conservatism by locally minimizing a certain asymptotic upper bound of the synchronization error dynamics. Finally, three illustrative simulation examples are utilized to validate the feasibility and effectiveness of the developed synchronization control scheme.

Adaptive Neural Event-Triggered Control of Networked Markov Jump Systems Under Hybrid Cyberattacks

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

Event-Triggered Recursive State Estimation for Stochastic Complex Dynamical Networks Under Hybrid Attacks

In this article, the event-based recursive state estimation problem is investigated for a class of stochastic complex dynamical networks under cyberattacks. A hybrid cyberattack model is introduced to take into account both the randomly occurring deception attack and the randomly occurring denial-of-service attack. For the sake of reducing the transmission rate and mitigating the network burden, the event-triggered mechanism is employed under which the measurement output is transmitted to the estimator only when a preset condition is satisfied. An upper bound on the estimation error covariance on each node is first derived through solving two coupled Riccati-like difference equations. Then, the desired estimator gain matrix is recursively acquired that minimizes such an upper bound. Using the stochastic analysis theory, the estimation error is proven to be stochastically bounded with probability 1. Finally, an illustrative example is provided to verify the effectiveness of the developed estimator design method.

Adaptive Neural State Estimation of Markov Jump Systems Under Scheduling Protocols and Probabilistic Deception Attacks

The neural-network (NN)-based state estimation issue of Markov jump systems (MJSs) subject to communication protocols and deception attacks is addressed in this article. For relieving communication burden and preventing possible data collisions, two types of scheduling protocols, namely: 1) the Round-Robin (RR) protocol and 2) weighted try-once-discard (WTOD) protocol, are applied, respectively, to coordinate the transmission sequence. In addition, considering that the communication channel may suffer from mode-dependent probabilistic deception attacks, a hidden Markov-like model is proposed to characterize the relationship between the malicious signal and system mode. Then, a novel adaptive neural state estimator is presented to reconstruct the system states. By taking the influence of deception attacks into performance analysis, sufficient conditions under two different scheduling protocols are derived, respectively, so as to ensure the ultimately boundedness of the estimate error. In the end, simulation results testify the correctness of the adaptive neural estimator design method proposed in this article.

Neural-Network-Based Set-Membership Fault Estimation for 2-D Systems Under Encoding-Decoding Mechanism

In this article, the simultaneous state and fault estimation problem is investigated for a class of nonlinear 2-D shift-varying systems, where the sensors and the estimator are connected via a communication network of limited bandwidth. With the purpose of relieving the communication burden and enhancing the transmission security, a new encoding-decoding mechanism is put forward so as to encode the transmitted data with a finite number of bits. The aim of the addressed problem is to develop a neural-network (NN)-based set-membership estimator for jointly estimating the system states and the faults, where the estimation errors are guaranteed to reside within an optimized ellipsoidal set. With the aid of the mathematical induction technique and certain convex optimization approaches, sufficient conditions are derived for the existence of the desired set-membership estimator, and the estimator gains and the NN tuning scalars are then presented in terms of the solutions to a set of optimization problems subject to ellipsoidal constraints. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed estimator design method.

Dynamic event-based sliding mode security control for singular Semi-Markov jump LPV systems against deception attacks

In this paper, we preliminarily propose the dissipative sliding mode control (SMC) scheme for polyhedral singular semi-Markov jump linear parameter varying (SS-MJLPV) systems considering deception attacks between the sensor and controller. The main feature of this scheme is that a novel developed parameter dependent integral-type SMC law follows the changes of the system. Note that the mode of the sliding mode controller is not synchronized with the system mode, and the transition rates (TRs) of the system are assumed to be unknown. Moreover, we extend the previous work concerning the static event-triggered transmission protocol (ETP) to the dynamic one, in which the triggering threshold is dynamically updated via the internal-dynamic-variable. Finally, a DC-motor model is presented to illustrate the correctness of the developed results.

Event-triggered stabilization for networked control systems under random occurring deception attacks

This paper copes with event-triggered stabilization for networked control systems subject to deception attacks. A new switched event-triggered scheme (ETS) is designed by introducing a term regarding the last triggering moment in the trigger condition. This increases the difficulty of triggering, thus reducing trigger times compared to some existing ETSs. Furthermore, to cater for actual deception attack behavior, the occurrence of deception attacks is assumed to be a time-dependent stochastic variable that obeys the Bernoulli distribution with probability uncertainty. By means of a piecewise-defined Lyapunov function, a sufficient condition is developed to assure that the close-loop system under deception attacks is exponentially stable in regards to mean square. On the basis of this, a joint design of the desired trigger and feedback-gain matrices is presented. Finally, a simulation example is given to confirm the validity of the design method.

Composite Antidisturbance Control for Non-Gaussian Stochastic Systems via Information-Theoretic Learning Technique

In this article, a novel composite hierarchical antidisturbance control (CHADC) algorithm aided by the information-theoretic learning (ITL) technique is developed for non-Gaussian stochastic systems subject to dynamic disturbances. The whole control process consists of some time-domain intervals called batches. Within each batch, a CHADC scheme is applied to the system, where a disturbance observer (DO) is employed to estimate the dynamic disturbance and a composite control strategy integrating feedforward compensation and feedback control is adopted. The information-theoretic measure (entropy or information potential) is employed to quantify the randomness of the controlled system, based on which the gain matrices of DO and feedback controller are updated between two adjacent batches. In this way, the mean-square stability is guaranteed within each batch, and the system performance is improved along with the progress of batches. The proposed algorithm has enhanced disturbance rejection ability and good applicability to non-Gaussian noise environment, which contributes to extending CHADC theory to the general stochastic case. Finally, simulation examples are included to verify the effectiveness of theoretical results.

Delay Compensation-Based State Estimation for Time-Varying Complex Networks With Incomplete Observations and Dynamical Bias

In this article, a delay-compensation-based state estimation (DCBSE) method is given for a class of discrete time-varying complex networks (DTVCNs) subject to network-induced incomplete observations (NIIOs) and dynamical bias. The NIIOs include the communication delays and fading observations, where the fading observations are modeled by a set of mutually independent random variables. Moreover, the possible bias is taken into account, which is depicted by a dynamical equation. A predictive scheme is proposed to compensate for the influences induced by the communication delays, where the predictive-based estimation mechanism is adopted to replace the delayed estimation transmissions. This article focuses on the problems of estimation method design and performance discussions for addressed DTVCNs with NIIOs and dynamical bias. In particular, a new distributed state estimation approach is presented, where a locally minimized upper bound is obtained for the estimation error covariance matrix and a recursive way is designed to determine the estimator gain matrix. Furthermore, the performance evaluation criteria regarding the monotonicity are proposed from the analytic perspective. Finally, some experimental comparisons are proposed to show the validity and advantages of the new DCBSE approach.

Simultaneous State and Unknown Input Estimation for Complex Networks With Redundant Channels Under Dynamic Event-Triggered Mechanisms

This article addresses the simultaneous state and unknown input estimation problem for a class of discrete time-varying complex networks (CNs) under redundant channels and dynamic event-triggered mechanisms (ETMs). The redundant channels, modeled by an array of mutually independent Bernoulli distributed stochastic variables, are exploited to enhance transmission reliability. For energy-saving purposes, a dynamic event-triggered transmission scheme is enforced to ensure that every sensor node sends its measurement to the corresponding estimator only when a certain condition holds. The primary objective of the investigation carried out is to construct a recursive estimator for both the state and the unknown input such that certain upper bounds on the estimation error covariances are first guaranteed and then minimized at each time instant in the presence of dynamic event-triggered strategies and redundant channels. By solving two series of recursive difference equations, the desired estimator gains are computed. Finally, an illustrative example is presented to show the usefulness of the developed estimator design method.

A Dynamic Event-Triggered Approach to Recursive Nonfragile Filtering for Complex Networks With Sensor Saturations and Switching Topologies

In this article, the nonfragile filtering issue is addressed for complex networks (CNs) with switching topologies, sensor saturations, and dynamic event-triggered communication protocol (DECP). Random variables obeying the Bernoulli distribution are utilized in characterizing the phenomena of switching topologies and stochastic gain variations. By introducing an auxiliary offset variable in the event-triggered condition, the DECP is adopted to reduce transmission frequency. The goal of this article is to develop a nonfragile filter framework for the considered CNs such that the upper bounds on the filtering error covariances are ensured. By the virtue of mathematical induction, gain parameters are explicitly derived via minimizing such upper bounds. Moreover, a new method of analyzing the boundedness of a given positive-definite matrix is presented to overcome the challenges resulting from the coupled interconnected nodes, and sufficient conditions are established to guarantee the mean-square boundedness of filtering errors. Finally, simulations are given to prove the usefulness of our developed filtering algorithm.

Mean-square bounded consensus of nonlinear multi-agent systems under deception attack

This paper researches mean-square bounded consensus for a nonlinear multi-agent system subjected to randomly occurring deception attack, process and measurement noises. Considering the measurement tampered by the attacker, an estimator is presented to obtain relative accurate state estimation, where the gain is acquired by a recursive algorithm. On this basis, a type of centralized controller is designed combined with cloud computing system. Moreover, from perspective of the defender, a detector is proposed at the side of agent to detect whether the current actuator input is attacked. Using linear matrix inequality, sufficient conditions are given for achieving mean-square bounded consensus and an upper boundary is derived. Finally, validity of the proposed method is illustrated via two simulation examples.

Resilient Event-Triggered Distributed State Estimation for Nonlinear Systems Against DoS Attacks

This article investigates the resilient event-triggered (ET) distributed state estimation problem for nonlinear systems under denial-of-service (DoS) attacks. Different from the existing results mainly considering linear or specified nonlinear systems, more general nonlinear systems are considered in this study. Moreover, the considered DoS attacks are able to compromise different communication links among estimators independently. In this context, by resorting to the techniques of incremental homogeneity, a nonlinear ET distributed estimation scheme is designed to estimate the states and regulate the data transmission. Under this scheme, the resilient state estimation is achieved by employing a multimode switching estimator, and the problem of efficiency loss of the ET mechanism caused by DoS attacks is solved by designing a dynamic trigger threshold with switched update laws. Then, based on the decay rates of the Lyapunov function corresponding to different communication modes, sufficient conditions are given to guarantee the stability of the estimation error system under DoS attacks. Finally, simulation results are provided to verify the effectiveness of the proposed method.

Secure filtering under adaptive event-triggering protocols with memory mechanisms

The paper investigates secure filtering of nonlinear large-scale systems suffering from randomly occurring DoS attacks. By introducing an adjustable parameter, an adaptive event-triggering mechanism is proposed for the sake of decreasing the transmission burden of signals, where the memory is utilized to reflect the influence of past triggered information. The main objective is to design an event-based secure filter to ensure that the dynamics of filtering errors is input-to-state stable in the mean square. Using the constructed Lyapunov function, a sufficient condition is derived where some element matrix inequalities are utilized to handle the inherent coupling of subsystems. Furthermore, the desired filter gains are parameterized by resorting to the feasibility of matrix inequalities. Finally, a numerical simulation about a power system is provided to verify the effectiveness of the developed secure filtering algorithm.

Estimator design for complex networks with encoding decoding mechanism and buffer-aided strategy: A partial-nodes accessible case

This article focuses on the partial-nodes-based state estimation (PNBSE) issue for a complex network with the encoding-decoding mechanism (EDM) over the unreliable communication channel, where the signals are transmitted in an intermittent manner. A so-called EDM is exploited to convert the transmitted signals into a set of codewords with finite bits so as to facilitate the transmission efficiency between the complex networks and the estimator. To guarantee the state estimation (SE) performance subject to the intermittent communication nature of the channel, a buffer with limited capacity, which stores the recent measurement signals and sends them to the estimator simultaneously, is adopted to improve the utilization rate of the measurement signals in the estimation process. The main objective of the investigated problem is to construct a partial-nodes-based (PNB) estimator to generate the desired state estimates for the underlying complex networks. Considering the intermittent feature of signal transmission, the ultimate boundedness of the SE error under the constructed PNB estimator is discussed, and then, sufficient conditions are derived which ensure that the desired PNB estimator exists. An simulation example is given to confirm the correctness and effectiveness of the proposed estimator design strategy in the end.

Event-based consensus control for multi-agent systems against joint sensor and actuator attacks

The paper focuses on the observer-based consensus control issue for a class of discrete-time multi-agent systems suffering from the joint actuator and sensor attacks. An event-triggered communication rule with the adaptive threshold update is introduced to reduce the communication burden. A PI-type controller with a finite-time window integral loop is constructed to achieve bounded consensus in the mean-square sense (BCMS). With the help of common properties of Laplace matrices, the closed-loop multi-agent system is converted into an easy-to-analyze pattern. In light of such a pattern, a sufficient condition is derived to realize the desired consensus by the stochastic analysis. Furthermore, the expected gains of the controller and the observer are determined by resorting to matrix inequalities in combination with the cone complementarity linearization algorithm. Finally, a numerical example and a simulation of the platooning vehicle are proposed to illustrate the effectiveness of the proposed control scheme.

Recursive state estimation for a class of nonlinear uncertain coupled complex networks subject to random link failures and packet disorders

This paper is concerned with the recursive state estimation (RSE) problem under minimum mean-square error sense for a class of nonlinear complex networks (CNs) with uncertain inner coupling, random link failures and packet disorders. Firstly, a set of random variables obeying the Bernoulli distribution is adopted to characterize whether there are connections between different network units, i.e., there is no random link failure when the random variable is equal to 1, otherwise the random link failure occurs. In addition, the inner coupling strength is assumed to be varying within a given interval and the phenomenon of packet disorders caused by the random transmission delay (RTD) is also taken into account. In our study, the nonlinearity satisfies the continuously differentiable condition, which can be linearized by resorting to the Taylor expansion. The focus of the addressed RSE problem is on the design of an RSE approach in the mean-square error sense such that, for all uncertain inner coupling, random link failures and packet disorders, a suboptimal upper bound of the state estimation error covariance is obtained and minimized by parameterizing the state estimator gain with explicit expression form. Furthermore, a sufficient condition with respect to the uniform boundedness of state estimation error in mean-square sense is elaborated. Finally, a numerical experiment is introduced to demonstrate the validity of the presented RSE approach.

A switching event-triggered resilient control scheme for primary and secondary levels in AC microgrids

In microgrid hierarchical control, primary control to stabilize system and secondary control to eliminate frequency/voltage deviations are both necessary for islanded microgrids. In this paper, a switching event-triggered (SET) resilient control scheme for microgrid primary and secondary levels has been proposed. First, droop control and model predictive control (MPC) are used for power sharing and driving signal generation respectively on primary level. An SET control method to trigger different MPC cost functions is proposed to balance output voltage quality and switching frequency. Then, distributed consensus and pinning control based method is used on secondary level. An SET mechanism to dynamically adjust secondary ratios to coordinate the transient deviation and response speed is proposed. Next, to deal with severe accidents, a resilient control scheme integrating primary and secondary levels is designed to maintain the system sound operation and improve the system immunity. Finally, the effectiveness of the proposed control scheme has been demonstrated by simulation with comprehensive scenarios.

Path Tracking Control of Autonomous Vehicles Subject to Deception Attacks via a Learning-Based Event-Triggered Mechanism

This article investigates the problem of event-triggered secure path tracking control of autonomous ground vehicles (AGVs) under deception attacks. To relieve the burden of the shareable vehicle communication network and to improve the tracking performance in the presence of deception attacks, a learning-based event-triggered mechanism (ETM) is proposed. Different from existing ETMs, the triggering threshold of the proposed mechanism can be dynamically adjusted with conditions of the latest vehicle state. Each vehicle in this study is deemed as an agent, under which a novel control strategy is developed for these autonomous agents with deception attacks. With the assistance of Lyapunov stability theory, sufficient conditions are obtained to guarantee the stability and stabilization of the overall system. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed theoretical results.

A Partial-Node-Based Approach to State Estimation for Complex Networks With Sensor Saturations Under Random Access Protocol

In this article, the robust finite-horizon state estimation problem is investigated for a class of time-varying complex networks (CNs) under the random access protocol (RAP) through available measurements from only a part of network nodes. The underlying CNs are subject to randomly occurring uncertainties, randomly occurring multiple delays, as well as sensor saturations. Several sequences of random variables are employed to characterize the random occurrences of parameter uncertainties and multiple delays. The RAP is adopted to orchestrate the data transmission at each time step based on a Markov chain. The aim of the addressed problem is to design a series of robust state estimators that make use of the available measurements from partial network nodes to estimate the network states, under the RAP and over a finite horizon, such that the estimation error dynamics achieves the prescribed H_∞ performance requirement. Sufficient conditions are provided for the existence of such time-varying partial-node-based H_∞ state estimators via stochastic analysis and matrix operations. The desired estimators are parameterized by solving certain recursive linear matrix inequalities. The effectiveness of the proposed state estimation algorithm is demonstrated via a simulation example.

Outlier-Resistant Recursive Filtering for Multisensor Multirate Networked Systems Under Weighted Try-Once-Discard Protocol

In this article, a new outlier-resistant recursive filtering problem (RF) is studied for a class of multisensor multirate networked systems under the weighted try-once-discard (WTOD) protocol. The sensors are sampled with a period that is different from the state updating period of the system. In order to lighten the communication burden and alleviate the network congestions, the WTOD protocol is implemented in the sensor-to-filter channel to schedule the order of the data transmission of the sensors. In the case of the measurement outliers, a saturation function is employed in the filter structure to constrain the innovations contaminated by the measurement outliers, thereby maintaining satisfactory filtering performance. By resorting to the solution to a matrix difference equation, an upper bound is first obtained on the covariance of the filtering error, and the gain matrix of the filter is then characterized to minimize the derived upper bound. Furthermore, the exponential boundedness of the filtering error dynamics is analyzed in the mean square sense. Finally, the usefulness of the proposed outlier-resistant RF scheme is verified by simulation examples.

Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay

This paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in which the attack occurs via both communication channels, from S-C and from C-A simultaneously, rather than considering only one, as in many papers; and the double attacks are described by high-level Markov processes rather than simple random variables. To further reduce network load, an advanced AETS with an adaptive threshold coefficient was first used in FNNs to deal with deception attacks. Moreover, given the engineering background, uncertain parameters and time-varying delay were also considered, and a feedback control scheme was adopted. Based on the above, a unique closed-loop synchronization error system was constructed. Sufficient conditions that guarantee the stability of the closed-loop system are ensured by the Lyapunov-Krasovskii functional method. Finally, a numerical example is presented to verify the effectiveness of the proposed method.

Nonfragile H∞ State Estimation for Recurrent Neural Networks With Time-Varying Delays: On Proportional-Integral Observer Design

In this article, a novel proportional-integral observer (PIO) design approach is proposed for the nonfragile H_∞ state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped with more design freedom leading to better steady-state accuracy compared with the conventional Luenberger observer. The phenomena of randomly occurring gain variations, which are characterized by the Bernoulli distributed random variables with certain probabilities, are taken into consideration in the implementation of the addressed PIO. Attention is focused on the design of a nonfragile PIO such that the error dynamics of the state estimation is exponentially stable in a mean-square sense, and the prescribed H_∞ performance index is also achieved. Sufficient conditions for the existence of the desired PIO are established by virtue of the Lyapunov-Krasovskii functional approach and the matrix inequality technique. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed PIO design scheme.

Genetic-Algorithm-Assisted Sliding-Mode Control for Networked State-Saturated Systems Over Hidden Markov Fading Channels

The sliding-mode control (SMC) problem is studied in this article for state-saturated systems over a class of time-varying fading channels. The underlying fading channels, whose channel fading amplitudes (characterized by the expectation and variance) are allowed to be different, are modeled as a finite-state Markov process. A key feature of the problem addressed is to use a hidden Markov mode detector to estimate the actual network mode. The novel model of hidden Markov fading channels (HMFCs) is shown to be more general yet practical than the existing fading channel models. Based on a linear sliding surface, a switching-type SMC law is dedicatedly constructed by just using the estimated network mode. By exploiting the concept of stochastic Lyapunov stability and the approach of hidden Markov models, sufficient conditions are obtained for the resultant SMC systems that ensure both the mean-square stability and the reachability with a sliding region. With the aid of the Hadamard product, a binary genetic algorithm (GA) is developed to solve the proposed SMC design problem subject to some nonconvex constraints induced by the state saturations and the fading channels, where the proposed GA is based on the objective function for optimal reachability. Finally, a numerical example is employed to verify the proposed GA-assisted SMC scheme over the HMFCs.

Proportional-Integral Observer Design for Multidelayed Sensor-Saturated Recurrent Neural Networks: A Dynamic Event-Triggered Protocol

In this article, the design problem of the proportional-integral observer (PIO) is investigated for a class of discrete-time multidelayed recurrent neural networks (RNNs). In the addressed RNN model, the delays occurring in the information interconnections are allowed to be different, and the phenomenon of sensor saturation is taken into consideration in the measurement model. A novel dynamic event-triggered protocol is employed in the data transmission from sensors to the observer with hope to improve the efficiency of resource utilization, where the threshold parameters are adaptive to the dynamical environment. By virtue of the Lyapunov-like approach, a general framework is established for examining the boundedness of the estimation errors in mean-square sense, and the ultimate bound of the error dynamics is also acquired. Subsequently, the explicit expression of the desired PIO is parameterized by using the matrix inequality techniques. Finally, a simulation example is utilized to verify the effectiveness and superiority of the proposed PIO design scheme.

An Event-Triggering Approach to Recursive Filtering for Complex Networks With State Saturations and Random Coupling Strengths

In this article, the recursive filtering problem is investigated for a class of time-varying complex networks with state saturations and random coupling strengths under an event-triggering transmission mechanism. The coupled strengths among nodes are characterized by a set of random variables obeying the uniform distribution. The event-triggering scheme is employed to mitigate the network data transmission burden. The purpose of the problem addressed is to design a recursive filter such that in the presence of the state saturations, event-triggering communication mechanism, and random coupling strengths, certain locally optimized upper bound is guaranteed on the filtering error covariance. By using the stochastic analysis technique, an upper bound on the filtering error covariance is first derived via the solution to a set of matrix difference equations. Next, the obtained upper bound is minimized by properly parameterizing the filter parameters. Subsequently, the boundedness issue of the filtering error covariance is studied. Finally, two numerical simulation examples are provided to illustrate the effectiveness of the proposed algorithm.

Dynamic event-based state estimation for delayed artificial neural networks with multiplicative noises: A gain-scheduled approach

This study is concerned with the state estimation issue for a kind of delayed artificial neural networks with multiplicative noises. The occurrence of the time delay is in a random way that is modeled by a Bernoulli distributed stochastic variable whose occurrence probability is time-varying and confined within a given interval. A gain-scheduled approach is proposed for the estimator design to accommodate the time-varying nature of the occurrence probability. For the sake of utilizing the communication resource as efficiently as possible, a dynamic event triggering mechanism is put forward to orchestrate the data delivery from the sensor to the estimator. Sufficient conditions are established to ensure that, in the simultaneous presence of the external noises, the randomly occurring time delays with time-varying occurrence probability as well as the dynamic event triggering communication protocol, the estimation error is exponentially ultimately bounded in the mean square. Moreover, the estimator gain matrices are explicitly calculated in terms of the solution to certain easy-to-solve matrix inequalities. Simulation examples are provided to show the validity of the proposed state estimation method.