This article investigates the event-triggered anti-disturbance control for multiagent systems (MASs) subjected to multiple disturbances and time-varying transmission delays (TDs). Unlike existing studies that only consider the abrupt changes in parameters or communication topologies, this work employs the dual-Markov jumping processes with a switching signal to describe stochastic behaviors based on a novel mapping technique. The dynamic event-triggered protocol (DETP) is established to reduce communication burdens by incorporating a packet loss schedule (PLS). Additionally, the composite anti-disturbance controllers are developed based on disturbance observers (DOs) and extended dissipative performance analysis. By employing Lyapunov-Krasovskii functional (LKF) and the Finsler lemma, the stabilization conditions of the switched dual-Markov jumping MAS (SDMJMAS) are derived. Finally, the effectiveness of the proposed methods is validated through comparative experiments.

The lag consensus problem of multi-unmanned aerial vehicle (UAV) systems under hybrid attacks is investigated in this paper. First, a dynamic memory event-triggered control protocol is proposed for the multi-UAV system whose normal network communication would be hindered by denial-of-service (DoS) attacks. Different from traditional event-triggered mechanisms, we consider both historically transmitted data and dynamic threshold in the dynamic memory event-triggered protocol, and it will make less data transmissions and better control performance. Second, due to the communication structure is not fixed, we establish a switching-topology-based distributed control architecture. In view of the fact that communication delays among agents cannot be ignored, a distributed controller is proposed to achieves lag consensus of the multi-UAV system. And then, an estimator is designed to address the situation which the system state cannot be measured during the control process. Additionally, the practicality of the distributed control scheme is analyzed by ruling out Zeno behavior. Ultimately, the effectiveness and validity of the proposed control scheme are confirmed through a simulation example.

In this article, we propose a new concept called average impulsive delay-gain (AIDG) for studying the synchronization of coupled neural networks (CNNs). Based on the viewpoints of impulsive control and impulsive perturbation, we establish some globally exponential synchronization criteria for CNNs. Our methods are well-suited for addressing the synchronization problems of systems subject to hybrid delayed impulses with time-varying impulsive delay and gain. Moreover, we prove that the AIDG has both positive and negative effects on synchronization. Compared to existing research, our conclusions are more applicable and less conservative as the considered hybrid delayed impulses involve more flexible cases. Finally, we validate the effectiveness of our proposed results by applying them to small-world and scale-free network models.

This article investigates a distributed fixed-time secondary control (FTSC) scheme to eliminate dc voltage deviations caused by voltage-droop control (VDC) in a multiterminal dc transmission system (MTDCTS) with switching topology and achieve precise power sharing within a fixed time frame. The distributed FTSC combines a dc voltage controller and a power sharing controller. The main objective is to restore the average dc voltage of the converters to dc voltage reference of MTDCTS within a fixed time. Additionally, it also enables power sharing among the converters based on their individual capacities. Compared to conventional distributed consensus control (DCC), fixed-time control (FTC) presented in this article exhibits a shorter convergence time and is unaffected by the initial state of MTDCTS. In this article, each converter communicates exclusively with its adjacent converters via a communication network that may undergo changes over time, which helps alleviate the strain on the communication network. To test the FTSC, a five-terminal MTDCTS with two wind farm converters (WFCs) is created in power systems computer aided design (PSCAD)/electromagnetic transients including DC (EMTDC).

In this article, the problem of distributed optimization subject to a convex set is studied by employing a multiagent system, where each agent only has access to a stochastic gradient of its own cost function, and exchanges local information with its neighbors through a time-varying digraph. To handle this problem, a stochastic distributed algorithm based on the clipped distributed strategy and the dual averaging strategy is proposed. Of particular interest follows that we study the high probability convergence of the stochastic distributed algorithm under the assumption that the stochastic gradient follows a heavy-tailed distribution. Heavy-tailed distributions often causes extreme values, which bring difficulties in achieving the high probability convergence. Under mild assumptions on the graph and cost functions, we prove that the difference between the cost function value generated by the algorithm and its minimum value converges with high probability. Finally, a simulation experiment is presented to demonstrate the effectiveness of our theoretical results.

Abstract The paper addresses the fixed-/preassigned-time synchronization of stochastic memristive neural networks (MNNs) with uncertain parameters and mixed delays. Adaptive sliding mode control (ASMC) technology is mainly utilized. First, a proper sliding surface is constructed and the adaptive laws are given. Also, the synchronization control scheme is designed, which can ensure error system to realize fixed-time stability. Second, preassigned-time sliding mode control scheme is mainly provided to realize fast synchronization of MNNs. The presented theoretical methods can guarantee the error system convergence and stability for reaching and sliding mode within preassigned-time. And the synchronization criteria and explicit expression of settling time (ST) are acquired, where ST is not related with initial values and controller parameters but can be predefined perferentially. Finally, the calculation example is offered to interpret the practicability and availability of the innovations in this paper.

This article investigates the practical fixed-time synchronization of uncertain coupled neural networks via dual-channel event-triggered control. Contrary to some previous studies, the bipartite synchronization of signed graphs representing cooperative and antagonistic interactions is studied. The communication channel is introduced into deception attacks, which are described by Bernoulli's stochastic variables. Based on the concept of two channels, event-triggered mechanisms are designed for sensor-to-controller and controller-to-actuator channels to reduce communication consumption and controller update consumption as much as possible. Lyapunov and comparison theories are used to derive synchronization criteria and explicit expression of settling time. An example of Chua's circuit system is presented to demonstrate the feasibility of the obtained theoretical results.

This article considers the rapid vibration reduction problem of macro–micro composite positioning stage (MMCPS) using an adaptive neural preassigned-time control strategy. Based on Newton's second law, the MMCPS is modeled as an interconnected system with unknown perturbations, and for the first time, the vibration reduction problem of MMCPS is transformed into a displacement constraint problem. Through adaptive neural network approximation and backstepping control, a preassigned-time controller with a novel performance function-related term is developed, which not only significantly improves the positioning accuracy and reduces the vibration amplitude but also ensures that the displacements of the voice coil motor axis and the stage are constrained to a predefined region in a finite time. Another distinguished feature of the proposed controller lies in the fact that the settling time of the displacement signals can be set as an arbitrary positive value. Moreover, all signals of the closed-loop system are proved to be semiglobally uniformly ultimately bounded. Finally, the feasibility of the designed control strategy is demonstrated via a simulation experiment.

This article studies bipartite consensus of multiagent systems (MASs) with input saturation and denial-of-service (DoS) attacks over weighted directed networks. First, a distributed control protocol is proposed by using low-gain technology to address the input saturation constraint. Then, an estimator is introduced to design a dynamic event-triggered communication protocol, which avoids continuous communication between agents. In addition, sufficient conditions for realizing bipartite security consensus are obtained under insecure communication networks with DoS attacks. Moreover, the dual-channel concept is considered to further save resources. An event-triggered controller protocol and a dynamic event-triggered communication protocol are designed in the communication channel and the controller–actuator channel, respectively. By considering an exponential threshold, the event-triggered controller protocol can operate stably when the control signal is minute. Thus, a novel dynamic event-triggered communication protocol is obtained to implement bipartite security consensus and exclude Zeno behavior. Finally, a practical example is presented to show the effectiveness of our design method.

In networked control systems (NCSs), the network-induced delay, packet dropouts, noise and other constraints in the communication network will affect the optimal tracking performance (OTP) and even the system’s stability, which is also a problem that the NCSs approach must solve in practical applications. In this study, we primarily examine the OTP of NCSs that consider packet dropouts and nonzero mean additive white noise (AWN) constraints in communication networks. Based on the single-degree-of-freedom (SDOF) controller and the two-degree-of-freedom (TDOF) controller, respectively, using the coprime factorization and Youla parameterization approach, the explicit expressions of the OTP limitation of the NCSs under the constraints of nonzero mean noise and packet dropouts are obtained. The results reveal that the intrinsic features of the plant and the communication parameters of the network channel will affect the OTP of the NCSs. Finally, the correctness of the theoretical results is verified by the simulation of a multi-input and multioutput plant and an inverted pendulum system.

Abstract The remarkable development of human–computer interactions has created an urgent need for machines to be able to recognise human emotions. Human motions play a key role in emphasising and conveying emotions to meet the complexity of daily application scenarios, such as medical rehabilitation and social education. Therefore, this paper aims to explore hidden emotional states from human motions. Accordingly, we proposed a novel approach for emotion recognition using multiple inertial measurement unit (IMU) sensors worn on different body parts. First, the mapping relationship between emotion and human motion was established through fuzzy comprehensive evaluation, and data were collected for six emotional states: sleepy, bored, excited, tense, angry, and distressed. Second, the preprocessed data were used as input in a lightweight convolutional neural network to extract discriminative features. Third, an attention‐based sensor fusion module was developed to obtain the importance scores of each IMU sensor for generating a fused feature representation. In the recognition phase, we constructed a weighted kernel support vector machine (SVM) model with an auxiliary fuzzy function to improve the weight calculation method of kernel functions in a multiple kernel SVM. Finally, the results obtained are compared with those of similar state‐of‐the‐art studies, the proposed method showed a higher accuracy (99.02%) for the six emotional states mentioned above. These findings may promote the development of social robots with non‐verbal emotional communication capabilities.

This article investigates the finite-time and fixed-time synchronization for memristive neural networks (MNNs) with mixed time-varying delays under the adaptive aperiodically intermittent adjustment strategy. Different from previous works, this article first employs the aperiodically intermittent adjustment feedback control and adaptive control to drive the MNNs to achieve synchronization in finite time and fixed time. First of all, according to the theories of set-valued mappings and differential inclusions, the error MNNs is derived, and its finite-time and fixed-time stability problems are discussed by applying the Lyapunov function method and some LMI techniques. Moreover, by meticulously designing an effective aperiodically intermittent adjustment with adaptive updating law, sufficient conditions that guarantee the finite-time and fixed-time synchronization of the drive-response MNNs are obtained, and the settling time is explicitly estimated. Finally, three numerical examples are provided to illustrate the validity of the obtained theoretical results.

In this article, a preassigned finite time control problem of nonlinear systems in strict-feedback form is investigated. From the perspective of arbitrary settling time, an appropriate preassigned finite-time performance function (PFPF) is constructed, and the preassigned finite-time stability (PAFS) is established, where the settling (convergence) time is not only completely unconcerned with initial conditions and design parameters but also more flexible. Furthermore, the backstepping technique and reduced-order observer are used to obtain the preassigned finite-time control scheme. The stability criteria of PAFS are developed to guarantee that the output can quickly converge to an arbitrarily small zone in preassigned time, and all signals of the closed-loop control system are PAFS. In the end, simulation examples verify the effectiveness of the presented method.

This paper studies the modeling problem of event- triggered networked hybrid dynamic games (HDGs). By considering the influence of an event-triggering mechanism, the evolution process of dynamic games with hybrid characteristics is analyzed, and we point out the complexity and technical difficulties in the analysis of such a game problem. From the perspective of network science, we give a viewpoint on network-based modeling of event-triggered HDGs. On the basis of the state-space model with established, we first give the normal form of HDGs by a seven-tuple. We then establish the directed dynamic network model of HDGs for the first time, involving a graph-based tree structure form, which can well describe the distinctive features of the continuous-time and discrete-event dynamic game process on both sides, and has great advantages in evolutionary analysis. An example of the evolution of event-triggered HDGs show the innovation of the proposed model.

For networked control systems, it is known that various communication parameters in the channel will pose some fundamental limitations on output tracking control (OTC) performance. In this study, we mainly discuss the limitations resulting from model uncertainties, involving channel and plant. Through using the bivariate stochastic process to model packet loss, and the assumption that channel noise is additive white Gaussian noise (AWGN), two explicit expressions of output tracking performance limitations are derived with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structure, which shows that the performance of OTC is closely related to the inherent characteristics of the plant, as well as the packet loss rate and power spectral density (PSD) of AWGN. Finally, by considering an illustrative example, the simulation results are verified and analyzed to ensure the effectiveness of treatment methods and results.

The output tracking control problem is investigated in this article. First, a new tradeoff performance index is presented for single-input-multioutput (SIMO) systems. Based on the frequency-domain method, the tracking performance limitations under time delay, packet loss, and channel noise effects are derived. We use a bivariate stochastic process to model the packet loss, and assume that channel noise is additive white Gaussian noise (AWGN). Two explicit expressions of the best tradeoff performance are given with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structures. It is shown that the tracking control performance has a close relation with the intrinsic characteristic of the plant, as well as the time delay, packet-dropouts rate, and power spectral density of AWGN. We also demonstrate that compared with the SDOF control structure, the TDOF control structure can improve the systems' attainable performance. A simulation example is finally discussed to validate the conclusions.

In this study, a class of finite-time consensus of multiple transmission control protocol/active queue management (TCP/AQM) networks is investigated on the basis of a design idea of multi-agent systems, and for the first time, to our knowledge, a novel congestion control concept with neural networks is proposed. First, the problem statement and design goal of the consensus of multiple TCP/AQM networks is given. Then, a pre-assigned finite-time function is introduced to ensure that the tracking error approaches a pre-defined area within finite time. Furthermore, by combining a barrier Lyapunov function and backstepping technique, a neural network-based distributed adaptive finite-time control protocol for the output consensus of multiple TCP/AQM networks is presented, which can effectively generate the desired controls and ensure that the convergent time of all errors has nothing to do with the initial condition and design parameters. In addition, all signals in the closed-loop system are bounded. Finally, an example is given to further illustrate the effectiveness of the theoretical finding presented.

This brief is devoted to exploring the global Mittag-Leffler (ML) synchronization problem of fractional-order memristor neural networks (FOMNNs) with leakage delay via a hybrid adaptive controller. By applying Fillipov's theory and the Lyapunov functional method, the novel algebraic sufficient condition for the global ML synchronization of FOMNNs is derived. Finally, a simulation example is presented to show the practicability of our findings.

This paper investigates an adaptive finite-time control (FTC) problem for a class of strict-feedback nonlinear systems with both time-delays and quantized input from a new point of view. First, a new concept, called preassigned finite-time performance function (PFTF), is defined. Then, another novel notion, called practically preassigned finite-time stability (PPFTS), is introduced. With PFTF and PPFTS in hand, a novel sufficient condition of the FTC is given by using the neural network (NN) control and direct adaptive backstepping technique, which is different from the existing results. In addition, a modified barrier function is first introduced in this work. Moreover, this work is first to focus on the FTC for the situation that the time-delay and quantized input simultaneously exist in the nonlinear systems. Finally, simulation results are carried out to illustrate the effectiveness of the proposed scheme.

In this paper, we address the finite-time tracking consensus control problem for nonlinear multi-agent systems under no-cycle communication graph. Unlike most existing works of finite-time consensus, we focus on nonlinear multi-agent systems with lower triangular subsystems. Based on the local cooperative information among neighboring agents, we propose a tracking consensus protocol ensuring that all agents achieve consensus in a finite time. Finally, we give an example to illustrate the effectiveness of the proposed protocols.

This paper analyzes multi-switching network transmission synchronization (MSNTS) problem among three uncertain chaotic systems with unknown parameters. By constructing the effective switching rules, the definition of MSNTS is given and the synchronization schemes are proposed to reach synchronization between any different states of each derive system and any desired states of every respond system by choosing the proper transmission path. Finally, simulation results show the feasibility of research results.