This paper describes our investigation of the privacy protection problem of multi-agent systems under cooperative–competitive networks. A node decomposition strategy is used to protect the privacy of the initial node values, in which a node v i is split into n i nodes. By designing inter-node weights, the initial value of each node is protected from honest-but-curious nodes and eavesdroppers without relying on external algorithms. The purpose is to design a privacy-preserving consensus algorithm such that the privacy performance is guaranteed by using the node decomposition strategy, while the bipartite consensus is achieved for the cooperative–competitive multi-agent systems. Two numerical simulations are given to validate the effectiveness of the proposed privacy-preserving bipartite consensus algorithm.

In this paper, the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems. The dynamical model of each agent is unknown and only the input/output data are available. A model-free adaptive control method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent. Then, by means of the distributed gradient descent method, a novel event-triggered model-free adaptive distributed optimization algorithm is put forward. Sufficient conditions are established to ensure the consensus and optimality of the addressed system. Finally, simulation results are provided to validate the effectiveness of the proposed approach.

This paper is concerned with the privacy-preserving distributed optimization problem for a class of cooperative-competitive multi-agent systems. Each agent only knows its own local objective function and interacts the state information with neighbors through a communication network. By means of the signed graph theory, the antagonistic interactions among agents are considered to characterize both the cooperative and the competitive relationships. With the help of the gauge transformation technique, a structurally balanced undirected signed graph is firstly transformed into a standard undirected graph. Then, the distributed optimization problem subject to signed network is converted into the traditional distributed optimization problem. Subsequently, a novel privacy-preserving distributed optimization algorithm is put forward to 1) minimize the sum of local objective functions; 2) achieve the bipartite consensus for all agents; and 3) avoid the information leakage caused by message exchange among agents, simultaneously. Finally, a simulation example is given to verify the effectiveness of the proposed optimization algorithm.

This article develops a joint diagnosis and estimation algorithm for state of charge of lithium-ion batteries subject to sensor measurement outlier. By means of the chi-square test mechanism, an online-outlier-detection method is put forward to detect and further diagnose the type of outliers. Compared with the traditional data-driven-based fault detection approach that relies on a great amount of historical data for training in which each iteration only requires the information from the previous moment such that the computational complexity relieves fairly. Different from the existing filtering methods, which are vulnerable to the corrupted measurements from the current and voltage sensor caused by unexpected outliers, this research involves measurement outliers in the design of the estimator. Then, combined with the extended Kalman filtering algorithm and the Holt's two-parameter linear exponential smoothing method (Holt method), an outlier-resistant Kalman filtering algorithm is proposed to prevent the outlier-induced effect from degrading the estimation performance. Finally, extensive experiments are conducted to validate the serviceability and practicability of the proposed strategy.

In this article, a proportional-integral-derivative (PID) tracking control problem is studied for a class of linear discrete-time systems under multiple description encoding mechanisms (MDEMs). The data transmissions on the sensor-to-controller channels are subject to packet dropouts whose occurrences are random and governed by two Bernoulli-distributed sequences of certain probability distributions. In order to improve the reliability of data transmission, an MDEM is put forward, with which the data is encoded into two descriptions of identical importance before being transmitted to the decoders through two individual communication channels. The aim of this article is to develop a PID tracking controller for guaranteeing the ultimate boundedness of the resulting tracking error, and the corresponding controller gains are obtained by solving an optimization problem. Moreover, the effect of the packet dropouts on the decoding accuracy is explicated via assessing the boundedness in respect to the decoding error. A simulation example is finally presented to showcase the applicability of the proposed PID tracking control scheme.

This paper is concerned with the secure estimation problem for the state of charge of Lithium-ion batteries subject to malicious attacks during the data transmission from sensors to cloud-based battery management system terminal. First, the second-order resistance-capacitance equivalent circuit model, whose parameters are identified by Kalman filter in an off-line manner, is introduced to describe the internal dynamics of lithium-ion batteries. Then, by applying the$\chi ^{2}$detection mechanism, real-time malicious attacks are first detected and then a secure estimator is designed to suppress the influence of attacks on the estimation performance. An upper bound of the filtering error covariance is determined by solving certain coupled Riccati-like equations, and the filter parameter is obtained by minimizing such an upper bound at each time step. Finally, the validity of the proposed attack detection approach and the effectiveness of the developed estimation scheme are verified by experiment results under Federal Urban Driving Schedule condition.

In this article, a pinning control strategy is developed for the finite-horizon$H_{\infty }$synchronization problem for a kind of discrete time-varying nonlinear complex dynamical network in a digital communication circumstance. For the sake of complying with the digitized data exchange, a feedback-type dynamic quantizer is introduced to reflect the transformation from the raw signals into the discrete-valued ones. Then, a quantized pinning control scheme takes place on a small fraction of the network nodes with the hope of cutting down the control expenses while achieving the expected global synchronization objective. Subsequently, by resorting to the completing-the-square technique, a sufficient condition is established to ensure the finite-horizon$H_{\infty }$index of the synchronization error dynamics against both quantization errors and external noises. Moreover, a controller design algorithm is put forward via an auxiliary$H_{2}$-type criterion, and the desired controller gains are acquired in terms of two coupled backward Riccati equations. Finally, the validity of the presented results is verified via a simulation example.

In this article, recursive filtering is investigated for time-varying discrete sequential systems (DSSs) under weighted try-once-discard (WTOD) protocols, which are employed to govern the access authorization of a shared network in order to remit the communication burden. A transmission model, dependent on a Bernoulli distributed white sequence, is developed to describe the phenomenon of deception attacks. In light of the adopted protocol and the attack model, a recursive algorithm with the form of Riccati-like difference equations is developed to optimize the filtering performance in the mean square sense. Furthermore, by resorting to the mathematical induction, the convergence of the proposed recursive algorithm is discussed profoundly. Finally, a simulation example is presented to verify the availability of the designed recursive filter.

In this article, we investigate the set-membership state estimation problem for a class of time-varying systems with non-Gaussian noises over a FlexRay network. To mitigate the communication load and improve the flexibility of the data scheduling, the FlexRay protocol (FRP) governed by both the time-triggered and event-triggered rules is exploited to regulate the signal transmission in a cyclic fashion. A new expression of the input signal to the state estimator is formulated with intention to account for the effect of the FRP. Accordingly, a multirate model (orchestrating the sampling/updating rates of the target plant, sensors, and state estimator) is proposed and then transformed into a single-rate one with the help of the lifting technique and the vector augmentation method. Subsequently, sufficient conditions are provided for the true states to always reside in an ellipsoid at each time instant in the presence of the non-Gaussian noises, and such an ellipsoid is then minimized in the matrix-trace sense. An online optimization algorithm is developed to parameterize the estimator gains by means of the solution to certain recursive matrix inequalities. Numerical results demonstrate the validity of the proposed protocol-based set-membership state estimator design scheme.

In this chapter, the partial-neurons-based state estimation problem is studied for a class of delayed neural networks with state-dependent noises under redundant channels. For the purpose of improving the success rate of the data transmission from the sensor to the estimator, the redundant-channel-based transmission mechanism is considered. The main aim of the addressed problem is to design a state estimator to estimate the neurons&s; state by use of a small fraction of the sensor measurements. With the help of the Lyapunov stability theory, a sufficient condition is provided to ensure that the estimation error dynamics is exponentially mean-square bounded. The desired estimator gain is acquired by minimizing an asymptotic upper bound of the estimation error. Finally, a numerical simulation is carried out to demonstrate the usefulness of the presented estimator design scheme.

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.

In this paper, the distributed set-membership filtering problem is dealt with for a class of time-varying multirate systems in sensor networks with the communication protocol. For relieving the communication burden, the round-Robin (RR) protocol is exploited to orchestrate the transmission order, under which each sensor node only broadcasts partial information to both the corresponding local filter and its neighboring nodes. In order to meet the practical transmission requirements as well as reduce communication cost, the multirate strategy is proposed to govern the sampling/update rate of the plant, the sensors, and the filters. By means of the lifting technique, the augmented filtering error system is established with a unified sampling rate. The main purpose of the addressed filtering problem is to design a set of distributed filters such that, in the simultaneous presence of the RR transmission protocol, the multirate mechanism, and the bounded noises, there exists a certain ellipsoid that includes all possible error states at each time instant. Then, the desired distributed filter gains are obtained by minimizing such an ellipsoid in the sense of the minimum trace of the weighted matrix. The proposed resource-efficient filtering algorithm is of a recursive form, thereby facilitating the online implementation. A numerical simulation example is given to demonstrate the effectiveness of the proposed protocol-based distributed filter design method.

This paper is concerned with the N-step model predictive control (MPC) problem for a class of constrained systems with persistent bounded disturbances under the stochastic communication protocol (SCP). The control signals are transmitted to the plant via a shared network subject to a prescribed SCP for the purpose of avoiding data collisions. The SCP scheduling, which is governed by a Markov chain, is applied to orchestrate the transmission order of the controller nodes. Under the SCP, only one control node is allowed to update the control signal sent to the plant at each communication instant. Our aim is to design a set of desired controllers in the framework of N-step MPC such that the mean-square input-to-state stability of the closed-loop system is guaranteed. An optimization algorithm consisting of both off-line and online parts is developed to cope with the design problem of the N-step controller. Finally, a numerical example is utilized to illustrate the validity of the proposed N-step MPC strategy.