The Internet of Things (IoT) increasingly relies on large-scale networked agents that cooperate over bandwidth-limited and unreliable wireless links. This paper studies the finite-horizonH∞consensus control problem for a class of discrete-time IoT-enabled multi-agent systems subject to random parameters, stochastic communication scheduling, and relay-assisted transmissions. To reflect practical IoT communication constraints, a novel transmission framework is considered that integrates a stochastic communication protocol with a decode-and-forward relay mechanism under random packet losses. Within this framework, only a subset of sensing information is scheduled for transmission, encoded, decoded, and forwarded by relay nodes to improve communication reliability and coverage. Based on the resulting networked system model, a distributed consensus controller is designed to attenuate the effects of disturbances and communication uncertainties over a finite time horizon. Sufficient conditions guaranteeing finite-horizonH∞consensus are derived in terms of recursive linear matrix inequalities, and a corresponding controller synthesis algorithm is developed. Simulation results illustrate the effectiveness of the proposed approach in achieving robust consensus for IoT-enabled multi-agent systems operating over stochastic relay-assisted communication networks.

This paper addresses the problem of input-output data-based ultimate boundedness control for a class of networked systems subject to probabilistic bit flips and false data injection (FDI) attacks under the try-once-discard (TOD) protocol. First, a prior experiment is conducted to obtain a set of input-output data from the considered system, which will be utilized for the data-based controller design. An uniform-quantization-based encoding-decoding mechanism is employed to digitalize measurement signals. The TOD protocol is adopted to schedule signal transmissions between encoders and decoders. Considering the nature of digital communication, an ellipsoid constraint and a sequence of Bernoulli variables are introduced to account for the FDI attacks and bit flips during the transmission, respectively. To expediently design the data-based controller, a novel autoregression-based method is proposed subject to probabilistic bit flips and protocol-induced effects. This paper aims to design a data-driven controller that ensures the ultimate boundedness of the closed-loop system under the effects of TOD protocol scheduling and communication failures. Sufficient conditions are presented to ensure the desired control performance by using the S-Lemma from data. An improved cone complementarity linearization (CCL) algorithm is developed to calculate the controller gain. Eventually, a numerical simulation example is provided to demonstrate the effectiveness and feasibility of the proposed data-based ultimate boundedness control scheme.

In this article, theH∞fuzzy control problem is investigated for a class of nonlinear systems subject to replay attacks with frequency-duration constraints. Owing to the vulnerability of the open shared communication network, the information transmitted from the sensor to the controller may be exposed to replay attackers. A novel yet comprehensive replay attack model is constructed to characterize the repeated replay behavior of the adversary. On the basis of the constructed model, a fuzzy controller is designed to guarantee asymptotic stability and the desiredH∞performance. By employing Lyapunov stability theory and the orthogonal decomposition technique, sufficient conditions are derived to ensure the existence of the desired controller parameter. Finally, simulation results are presented to verify the effectiveness and correctness of the proposed fuzzy controller for T-S fuzzy systems under replay attacks.

This article is concerned with the recursive state estimation issue for a class of nonlinear cyber-physical systems (CPSs) with token bucket protocols (TBPs) subject to sensor failures and false data injection (FDI) attacks. In the system under consideration, measurement signals are transmitted to the remote estimator only when there are sufficient tokens in the bucket to meet the token consumption. During network transmissions, the signals are exposed to FDI attacks, which occur randomly and follow a Bernoulli distribution. The primary objective is to develop a state estimation algorithm that can handle the TBP, sensor failures, and FDI attacks simultaneously. Initially, the upper bound of the estimation error covariance is derived using an intensive stochastic technique and the induction approach. Subsequently, the desired estimator gains are recursively computed to minimize this upper bound. Finally, an example is presented to demonstrate the effectiveness of the proposed estimation scheme.

This paper addresses the problem of distributed state estimation for smooth nonlinear systems over sensor networks by means of a generalized fuzzy proportional-integral observer (PIO). A sensor network is employed to collect system measurements, with a pull-type gossip protocol governing the intermittent data exchange among neighboring nodes. Under the gossip protocol, each sensor node randomly selects one neighbor to request data, facilitating distributed information updating. Furthermore, considering challenges such as long-distance communication and complex environmental conditions, signal transmission is subject to amplitude fading. To accommodate the characteristics of the gossip protocol, a generalized fuzzy PIO with a flexible structure is developed. Sufficient conditions are derived to guarantee the$H\_{\infty }$estimation performance of the proposed observer. Based on established conditions, the parameters of both the gossip protocol and the fuzzy PIO are co-designed via a particle-swarm-optimization-based iterative algorithm, with emphasis on enhancing observer robustness. Finally, an engineering-oriented simulation example is presented to illustrate the effectiveness of the proposed methodology.

This paper addresses the issue of robust finite-horizon H∞ filtering for complex networks subject to replay attacks. A replay attack strategy is implemented by the adversary on the communication channel between the network nodes and the filters, with the intention of replacing the current measurement data with previously recorded measurement data. Considering the limited energy of the attacker, a binary variable is adopted to indicate whether the communication channel is under attack. To better characterize the dynamic behavior of replay attacks, two factors dependent on attack frequency and a time-varying parameter are introduced. Subsequently, under the impact of replay attacks, the switched filtering error dynamics is obtained with a time-varying delay. By employing the average dwell-time method, sufficient conditions are derived to guarantee the weighted H∞ performance of the filtering error dynamics. Furthermore, the filter gain parameters are computed through the solution of some recursive matrix inequalities. Finally, numerical simulation results are conducted to verify that the developed filter design algorithm is effective.

This article investigates the proportional-integral-derivative (PID) containment control problem for a class of linear MAS with multirate measurements under the constraint of sensor resolution. The sensors of agents are classified into two distinct groups, characterized by their relatively fast and slow sampling periods. The concept of sensor resolution is introduced to quantify the ability of sensors to detect the smallest changes in information. A PID controller with an improved structure is proposed to achieve containment control, ensuring that follower agents remain within the convex hull formed by the leader agents. The closed-loop system is reformulated into a simplified representation, incorporating both sampling characteristics and communication topology. Sufficient conditions are then derived to guarantee the exponentially ultimate boundedness of the tracking error. Based on these conditions, an iterative algorithm is developed for computing the required controller gains. Finally, a simulation study, along with comparative analyses, is conducted to validate the effectiveness of the proposed approach.

This paper is concerned with the$H_{\infty }$proportional-integral-derivative (PID) control problem for Takagi-Sugeno fuzzy systems over lossy networks that are characterized by the Gilbert-Eillott model. The communication quality is reflected by the presence of two channel modes (i.e., “bad” mode and “good” mode), which switch randomly according to a Markov process. In the “bad” mode, packet dropouts are governed by a stochastic variable sequence. Considering the inaccessibility of channel modes, a mode detector is utilized to estimate the communication situation. The relationship between the actual channel mode and the estimated mode is depicted in terms of certain conditional probabilities. Moreover, a comprehensive model is constructed to represent the probability uncertainties arising from statistical errors in channel mode switching, packet dropouts, and mode detection processes. Subsequently, a robust asynchronous PID controller, based on the detected channel mode, is proposed. Sufficient conditions are then derived to ensure the mean-square stability of the closed-loop system while maintaining the desired$H_{\infty }$performance. Finally, the efficacy of the proposed design approach is demonstrated through a simulation example.

In this article, the design issue for an iterative learning controller is investigated for the cooperative-antagonistic system under the scheduling effects of random access protocol (RAP). In order to reflect the heterogeneous characteristic of the underlying system, the dynamics of each node in the cooperative-antagonistic system are described by a two-time-scale system. For the purpose of avoiding data collisions in signal transmissions, the so-called RAP is introduced to schedule the data exchanges among nodes, where the transmission opportunities of nodes are modeled by a sequence of random variables with certain transition probabilities. Considering that the mutual relationships may be cooperative and also competitive among nodes in many real-world networks, a novel cooperative-antagonistic-based iterative learning controller is developed to handle the tracking problem of the system dynamics. Sufficient conditions are obtained for the design of the controller parameters. Furthermore, the derived results are extended to the case that the transition probabilities for the RAP are partially unknown. Finally, a numerical example is presented to illustrate the effectiveness of the proposed iterative learning control (ILC) scheme.

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

This article is concerned with the distributed set-membership fusion estimation problem for a class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism (ETM) is utilized to schedule the signal transmission from sensors to local estimators to save resource consumption and avoid data congestion. The main purpose of this article is to design a distributed set-membership fusion estimation algorithm that ensures the global estimation error resides in a zonotope at each time instant and, meanwhile, the radius of the zonotope is ultimately bounded. By means of the zonotope properties and the linear matrix inequality (LMI) technique, the zonotope restraining the prediction error is first calculated to improve the prediction accuracy and subsequently, the zonotope enclosing the local estimation error is derived to enhance the estimation performance. By taking into account the side-effect of the order reduction technique (utilized in designing the local estimation algorithm) of the zonotope, a sufficient condition is derived to guarantee the ultimate boundedness of the radius of the zonotope that encompasses the local estimation error. Furthermore, parameters of the local estimators are obtained via solutions to certain bilinear matrix inequalities. Moreover, the zonotope-based distributed fusion estimator is obtained through minimizing certain upper bound of the radius of the zonotope (that contains the global estimation error) according to the matrix-weighted fusion rule. Finally, the effectiveness of the proposed distributed fusion estimation method is illustrated via a numerical example.

In this article, we address the design issue of the proportional-integral observer (PIO) for partially state-saturated systems, which are affected by probabilistic quantizations and an accumulation-based event-triggered mechanism (ABETM). A comprehensive model is established to characterize partial state saturations, wherein only a part of the state variables are saturated while the remaining variables maintain normal conditions. To conserve communication resources, an ABETM is employed to determine the release of system measurements to the PIO. Before transmission over the communication network, the triggered signal is quantized through a probabilistic quantization mechanism. The goal of this article is to devise a PIO that guarantees ultimate boundedness of the estimation error dynamics in the mean square. Initially, a sufficient condition is derived to ensure that the estimation error dynamics are exponentially ultimately bounded in the mean square. Following this, necessary PIO gains are identified by solving specific matrix inequalities, and the efficacy of this proposed PIO approach is validated using a three-tank system simulation.

This article investigates the recursive state estimation problem for a class of nonlinear cyber-physical systems (CPSs) operating under a token bucket strategy regulated by a random access protocol (RAP). Communication between sensor nodes and the remote estimator takes place over a shared network, where only one sensor node is permitted to access the network at each time instant to prevent data collisions. The transmission sequence of sensor nodes is governed by RAP scheduling, which is modeled as a sequence of independent and identically distributed variables representing the selected node granted network access. To efficiently manage limited communication resources, a token bucket strategy is employed. The measurement signal from the selected node is transmitted to the estimator only if a sufficient number of tokens are available in the bucket to meet the required token consumption. The objective is to design a state estimation algorithm that minimizes the estimation error covariance (EEC) by appropriately determining the estimator gain at each time step. The desired estimator gain is computed recursively by solving two Riccati-like difference equations. Finally, an illustrative example is presented to validate the effectiveness of the proposed estimation method.

This article deals with the distributed proportional–integral state estimation problem for nonlinear systems over sensor networks (SNs), where a number of spatially distributed sensor nodes are utilized to collect the system information. The signal transmissions among different sensor nodes are realized via their individual channels subject to energy-constrained Denial-of-Service (EC-DoS) cyber-attacks launched by the adversaries whose aim is to block the nodewise communications. Such EC-DoS attacks are characterized by a sequence of attack starting time-instants and a sequence of attack durations. Based on the measurement outputs of each node, a novel distributed fuzzy proportional–integral estimator is proposed that reflects the topological information of the SNs. The estimation error dynamics is shown to be regulated by a switching system under certain assumptions on the frequency and the duration of the EC-DoS attacks. Then, by resorting to the average dwell-time method, a unified framework is established to analyze the dynamical behaviors of the resultant estimation error system, and sufficient conditions are obtained to guarantee the stability as well as the weighted$H_{\infty}$performance of the estimation error dynamics. Finally, a numerical example is given to verify the effectiveness of the proposed estimation scheme.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

In this paper, we investigate the so-called$H_{\infty }$fuzzy proportional-integral observer (PIO) design problem for a class of nonlinear systems subject to relay effects, data missing, and multi-rate measurements. The considered multi-rate phenomenon is defined as the employment of sensors with diverse sampling periods due to specific engineering requirements. During the long transmission from multi-rate sensors to the remote fuzzy observer, the amplify-and-forward relay scheme is utilized to facilitate data communication, in which the measurement outputs are first directed to the relay nodes and then sent to the observer side. A unified model, incorporating both fast and slow sampling, is described using the switching system method. Subsequently, a fuzzy PIO is proposed by utilizing estimated premise variables along with current and historical system information. Through the application of stochastic analysis theory, the error dynamics of the state estimation is examined, and the observer gain matrices are determined by solving a specific convex optimization problem. Ultimately, two simulation experiments are conducted to validate the efficacy and utility of the formulated fuzzy PIO.

In this article, the problem of observer-based fuzzy proportional–integral–derivative (PID) tracking control is studied for networked nonlinear systems subject to protocol constraints and norm-bounded noises. The nonlinear plant under consideration is represented by an affine fuzzy model with immeasurable premise variables. The utilization of the try-once-discard protocol is proposed for information exchange between sensors and the controller, in order to mitigate the data transmission burden. An observer-based PID controller is put forward to achieve the desired tracking task and handle immeasurable premise variables, with sufficient consideration given to both available measurements and the reference trajectory. By analyzing the dynamics of the controlled tracking error system through the construction of a piecewise Lyapunov-like functional, the exponential ultimate boundedness of the tracking error dynamics is ensured. The controller parameters are designed using convex optimization technique and matrix theory, such that the tracking error dynamics is exponentially ultimately bounded. Finally, the validity and merits of the developed controller design method are verified through a simulation example.

This article addresses the problem of observer-based fuzzy proportional-integral-derivative (PID) control for a class of nonlinear systems subject to degraded measurements and randomly perturbed sampling periods (RPSPs). In the existing results, the degraded measurements and RPSPs are handled separately, where the sampling of different sensors is usually assumed to be synchronous. In our work, a comprehensive model is built to reflect the joint effects of degraded measurements and RPSPs by using a series of stochastic variable sequences and a set of Markov processes. In this model, the sampling periods of each sensor are allowed to be diverse, time-varying, and randomly perturbed, thereby fully capturing the environmental effects and device constraints. Different from the existing literature that uses proportional type controllers, an observer-based fuzzy PID controller with a modified structure is proposed, which fully utilizes the system information. To overcome the difficulties of the incomplete measurement information, some auxiliary variables related to the sampling periods are introduced under which the measurement output is transformed into a form delayed with stochastic delays. Subsequently, by using the special variable separation and inequality technique, sufficient conditions are derived to ensure the exponentially ultimate boundedness of the closed-loop system in the mean-square sense. The desired gains for the observer and PID controller are obtained through the solution of an optimization problem. Last, the effectiveness of the developed approach is demonstrated through simulation examples.

In this article, the sequential fusion estimation problem is investigated for multirate complex networks (MRCNs) with uniformly quantized measurements. The process and measurement noises, which are unknown-yet-bounded (UYB), are restrained into a family of zonotopes, and the multiple sensors are allowed to have different sampling periods. To facilitate digital transmissions, the sensor measurements are uniformly quantized before being sent to the remote estimator. The purpose of this article is to design a sequential set-membership estimator such that, in the simultaneous presence of UYB noises, multirate samplings, and uniform quantization effects, the estimation error (after each measurement update) is confined to a zonotope with minimum F -radius at each time instant. By introducing certain virtual measurements, the MRCNs are first transformed into single-rate ones exhibiting a switching phenomenon. Then, by utilizing the properties of zonotopes, the desired zonotopes are derived, which contain the estimation error dynamics after each measurement update. Subsequently, the gain matrices of the sequential estimator are derived by minimizing the F -radii of these zonotopes, and the uniform boundedness is analyzed for the F -radius of the zonotope containing the estimation error after all measurement updates. Furthermore, sufficient conditions are derived to ensure the existence of the desired uniform upper/lower bounds. Finally, an illustrated example is proposed to show the effectiveness of the proposed sequential fusion estimation method.

In this article, the tracking control problem is investigated for a type of linear networked systems subject to the round-Robin (RR) protocol scheduling and impulsive transmission outliers (ITOs). The communication between the controller and sensors is implemented through a shared network, on which the signal transmissions are scheduled by the RR protocol. The considered ITOs are modeled by a sequence of impulsive signals whose amplitudes (i.e., the norms of all impulsive signals) and interval lengths (i.e., the duration between all adjacent impulsive signals) are greater than two known thresholds, respectively. The occurrence moment for each ITO is first examined by using a certain outlier detection approach, and then a novel parameter-dependent tracking controller is proposed to protect the tracking performance from ITOs by removing the "harmful" signals (i.e., the transmitted signals contaminated by ITOs). Sufficient conditions are presented to ensure the exponentially ultimate boundedness of the resulted tracking error, and the controller gain matrices are subsequently designed by solving a constrained optimization problem. Finally, a simulation example is provided to demonstrate the effectiveness of our developed outlier-resistant tracking control scheme.

This article investigates the ultimately bounded proportional–integral–derivative (PID) control problem for a class of discrete-time Takagi–Sugeno fuzzy systems subject to unknown-but-bounded noises and protocol constraints. The signal transmissions from sensors to the remote controller are realized via a communication network, where the FlexRay protocol is employed to flexibly schedule the information exchange. The FlexRay protocol is characterized by both the time- and event-triggered mechanisms, which are conducted in a cyclic manner. By using a piecewise approach, the measurement outputs affected by the FlexRay protocol are established based on a switching model. Then, a fuzzy PID controller is proposed with a concise and realizable structure. To evaluate the performance of the controlled system, a special time sequence is introduced that accounts for the behavior of the FlexRay protocol. Subsequently, a general framework is obtained to verify the boundedness of the closed-loop system, and then, the controller gains are designed by minimizing the bound of the concerned variables. Finally, a simulation study is conducted to validate the effectiveness of the developed control scheme.

This article is concerned with the partial-node-based (PNB) state estimation problem for delayed complex networks (DCNs) subject to intermittent measurement outliers (IMOs). In order to describe the intermittent nature of outliers, several sequences of shifted gate functions are adopted to model the occurrence moments and the disappearing moments of IMOs. Two outlier-related indices, namely, minimum and maximum interval lengths, are employed to parameterize the "occurrence frequency" of IMOs. The norm of the addressed outlier is allowed to be greater than a certain fixed threshold, and this distinguishes the outlier from the extensively studied norm-bounded noise. By adopting the input-output models of the considered complex network, a novel multiple-order-holder (MOH) approach is developed to resist the effects of IMOs by dedicatedly designing a weighted average of certain non-IMO measurements, and then, a PNB state estimator is constructed based on the outputs of the MOHs. Sufficient conditions are proposed to ensure the exponentially ultimate boundedness (EUB) of the resultant estimation error, and the estimator gain matrices are subsequently obtained by solving a constrained optimization problem. Finally, two simulation examples are provided to demonstrate the effectiveness of our developed outlier-resistant PNB state estimation scheme.

This article is concerned with the multiloop decentralized$H_{\infty }$fuzzy proportional–integral–derivative-like (PID-like) control problem for discrete-time Takagi–Sugeno fuzzy systems with time-varying delays under dynamical event-triggered mechanisms (ETMs). The sensors of the plant are grouped into several nodes according to their physical distribution. For resource-saving purposes, the signal transmission between each sensor node and the controller is implemented based on the dynamical ETM. Taking the node-based idea into account, a general multiloop decentralized fuzzy PID-like controller is designed with fixed integral windows to reduce the potential accumulation error. The overall decentralized fuzzy PID-like control scheme involves multiple single-loop controllers, each of which is designed to generate the local control law based on the measurements of the corresponding sensor node. These kinds of local controllers are convenient to apply in practice. Sufficient conditions are obtained under which the controlled system is exponentially stable with the prescribed$H_{\infty }$performance index. The desired controller gains are then characterized by solving an iterative optimization problem. Finally, a simulation example is presented to demonstrate the correctness and effectiveness of the proposed design procedure.

In this article, we consider the$H_{\infty }$proportional-integral (PI) state estimation (SE) problem for discrete-time T–S fuzzy systems subject to transmission delays, external disturbances, and redundant channels. Multiple redundant communication channels are utilized between the sensors and the remote estimator to enhance the reliability of data transmissions. In order to characterize the transmission delays in network-based communication, a family of random variables with partly known probabilities, which are independent and identically distributed, is adopted to describe the random behavior of the transmission delays with the redundant channels. The objective of this work is to put forward a PI state estimator such that the dynamics of the estimation error is exponentially mean-square stable and satisfies the prescribed$H_{\infty }$performance index of the disturbance attenuation/rejection. By employing the stochastic analysis approach, the error dynamics of the SE under the proposed state estimator is analyzed and sufficient conditions are obtained to ensure the existence of the required PI state estimator. Furthermore, the desired estimator parameters are derived by solving a nonlinear optimization problem. Finally, two simulation examples are exploited to demonstrate the validity of the proposed SE scheme.

This article is concerned with the energy-to-peak state estimation problem for a class of linear discrete-time systems with energy-bounded noises and intermittent measurement outliers (IMOs). In order to capture the intermittent nature, two sequences of step functions are introduced to model the occurrence of the IMOs. Furthermore, two special indices (i.e., minimum and maximum interval lengths) are adopted to describe the "occurrence frequency" of IMOs. Different from the considered energy-bounded noises, the outliers are assumed to have their magnitudes larger than certain thresholds. In order to achieve a satisfactory performance constraint on the energy-to-peak state estimation under the addressed kind of measurement outliers, a novel parameter-dependent (PD) state estimation strategy is developed to guarantee that the measurements contaminated by outliers would be removed in the estimation process. The proposed PD state estimation method is essentially a two-step process, where the first step is to examine the appearing and disappearing moments for each IMO by using a dedicatedly constructed outlier detection scheme, and the second step is to implement the state estimation task according to the outlier detection results. Sufficient conditions are obtained to ensure the existence of the desired estimator, and the gain matrix of the desired estimator is then derived by solving a constrained optimization problem. Finally, a simulation example is presented to illustrate the effectiveness of our developed PD state estimation strategy.

This article is concerned with the$H_{\infty }$proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed$H_{\infty }$performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the$H_{\infty }$disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.

This article is concerned with the extended dissipative fuzzy proportional–integral–derivative (PID) control problem for nonlinear systems subject to controller parameter perturbations over a class of mixed fading channels. The sensors of plant are divided into two groups according to engineering practice, where the individual sensor group transmits the measurements to the controller via a respective communication channel undergoing specific fading effects. Considering the complicated nature of the signal fading with the transmission channels, two stochastic models (i.e., the independent and identically distributed fading model and the Markov fading model) are simultaneously employed to describe the mixed fading effects of the two communication channels corresponding to the two sensor groups. The objective of this article is to design a nonfragile PID controller such that the closed-loop system is exponentially stable in mean square and extended stochastically dissipative. With the assistance of the Lyapunov stability theory and stochastic analysis method, sufficient conditions are obtained to analyze the system performance. Then, within the established theoretical framework, an iterative optimization algorithm is proposed to design the desired controller parameters by using the convex optimization technique. Finally, two simulation examples are given to verify the effectiveness of the proposed control schemes.

The finite-time state estimation issue is addressed in this paper for discrete time-delayed neural networks (NNs). More than one communication channel is utilized to improve the communication performance. The transmission delays of each channel are modeled by a family of stochastic variables which are independent and identically distributed. The main purpose of this paper is to construct an appropriate state estimation scheme under which the corresponding state estimation error dynamics is finite-time bounded in the mean square. By employing the stochastic analysis approach and introducing a special Lyapunov-like functional, we have developed certain sufficient conditions to achieve the prescribed estimation performance. Furthermore, the exact expressions of the achieved estimator parameters are given by solving a special minimization problem subject to certain inequality constraints. Finally, we propose an illustrative simulation to examine the correctness, as well as the effectiveness, of our proposed state estimation method.

The full information estimation (FIE) problem is addressed for discrete time-varying systems (TVSs) subject to the effects of a round-Robin (RR) protocol. A shared communication network is adopted for data transmissions between sensor nodes and the state estimator. In order to avoid data collisions in signal transmission, only one sensor node could have access to the network and communicate with the state estimator per time instant. The so-called RR protocol, which is also known as the token ring protocol, is employed to orchestrate the access sequence of sensor nodes, under which the chosen sensor node communicating with the state estimator could be modeled by a periodic function. A novel recursive FIE scheme is developed by defining a modified cost function and using a so-called “backward-propagation-constraints.” The modified cost function represents a special global estimation performance. The solution of the proposed FIE scheme is achieved by solving a minimization problem. Then, the recursive manner of such a solution is studied for the purpose of online applications. For the purpose of ensuring the estimation performance, sufficient conditions are obtained to derive the upper bound of the norm of the state estimation error (SEE). Finally, two illustrative examples are proposed to demonstrate the effectiveness of the developed estimation algorithm.

This paper is concerned with the moving horizon (MH) estimation issue for a type of networked nonlinear systems (NNSs) with the so-called random access (RA) protocol scheduling effects. To handle the signal transmissions between sensor nodes and the MH estimator, a constrained communication channel is employed whose channel constraints implies that at each time instant, only one sensor node is permitted to access the communication channel and then send its measurement data. The RA protocol, whose scheduling behavior is characterized by a discrete-time Markov chain (DTMC), is utilized to orchestrate the access sequence of sensor nodes. By extending the robust MH estimation method, a novel nonlinear MH estimation scheme and the corresponding approximate MH estimation scheme are developed to cope with the state estimation task. Subsequently, some sufficient conditions are established to guarantee that the estimation error is exponentially ultimately bounded in mean square. Based on that the main results are further specialized to linear systems with the RA protocol scheduling. Finally, two numerical examples and the corresponding figures are provided to verify the effectiveness/correctness of the developed MH estimation scheme and approximate MH estimation scheme.

Train positioning is of critical importance for communication-based train control (CBTC) systems. The objective of this paper is to provide an algorithm to generate the precise estimates of the train position and velocity for CBTC systems with carrier-sense multiple access (CSMA) protocol scheduling, thereby improving the accuracy of train positioning as well as the availability of CBTC systems. First, the dynamics of a train with N cars linked by couplers is described based on Newton's motion equations. Then, the transmission model reflecting the behaviors of p-persistent CSMA protocol is presented by using a Bernoulli distributed sequence whose probability distribution is dependent on the number of trains sharing with one communication channel [i.e., N(k)]. Furthermore, the value of N(k) is assumed to be unknown but bounded by two known positive integers. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded (with a certain asymptotic upper bound) in mean square subject to the external resistive force. By utilizing the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. For the purpose of designing the desired estimator gains under different requirements (e.g., smallest ultimate bound and fastest decay rate), two optimization problems are solved in terms of linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of the estimator design scheme.

This paper is concerned with the distributed ℋ∞ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system. A sequence of random variables is utilized to capture the scheduling behavior of the SCP. By using the mapping technology combined with the Hadamard product, the closed-loop multiagent system is modeled as a time-varying system with a stochastic parameter matrix. The purpose of the addressed problem is to design a cooperative controller for each agent such that, for all probabilistic scheduling behaviors, the ℋ∞ consensus performance is achieved over a given finite horizon for the closed-loop multiagent system. A necessary and sufficient condition is derived to ensure the ℋ∞ consensus performance based on the completing squares approach and the stochastic analysis technique. Then, the controller parameters are obtained by solving two coupled backward recursive Riccati difference equations. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme.

In this paper, the event-based finite-horizon H∞state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with stateand disturbance-dependent noises [also called (x, v)-dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H∞performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

This paper is concerned with the state estimation problem for a class of nonlinear dynamical networks with time-varying delays subject to the round-robin protocol. The communication between the state estimator and the nodes of the dynamical networks is implemented through a shared constrained network, in which only one node is allowed to send data at each time instant. The round-robin protocol is utilized to orchestrate the transmission order of nodes. By using a switch-based approach, the dynamics of the estimation error is modeled by a periodic parameter-switching system with time-varying delays. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded with a certain asymptotic upper bound in mean square subject to the process noise and exogenous disturbance. Furthermore, such a bound is subsequently minimized by the designed estimator parameters. A novel Lyapunov-like functional is employed to deal with the dynamics analysis issue of the estimation error. Sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square by applying the stochastic analysis approach. Then, the desired estimator gains are characterized by solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the estimator design scheme.

In this paper, the event-triggered state estimation problem is investigated for a class of complex networks with mixed time delays using sampled data information. A novel state estimator is presented to estimate the network states. A new event-triggered transmission scheme is proposed to reduce unnecessary network traffic between the sensors and the estimator, where the sampled data is transmitted to the estimator only when the so-called "event-triggered condition" is satisfied. The purpose of the problem addressed is to design an estimator for the complex network such that the estimation error is ultimately bounded in mean square. By utilizing Lyapunov theory combined with the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. Then, the desired estimator gain matrices are obtained via solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the results.