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In this paper, we are concerned with the dynamic feedback design for linear systems with input disturbance. We propose a new approach to design dynamic feedback such that the disturbance in the control channel can be compensated effectively. An interesting characteristic about the disturbance robustness of dynamic feedback is found. Thanks to this interesting characteristic, we just need to consider the disturbance-free system to make the dynamic feedback design. This fact is well validated by several dynamic feedback laws proposed in this paper. Our results are available for the regular linear system with harmonic disturbance. Moreover, the stabilization of an unstable heat equation with general periodic disturbance is also investigated. In order to validate the theoretical results visually, some numerical simulations are given to show the effectiveness of the proposed dynamic feedback. [ABSTRACT FROM AUTHOR]

We consider the inherent attack tolerance properties of resilient model predictive control (R-MPC) for cyber–physical systems (CPSs) modeled by discrete linear time-invariant (LTI) systems subjected to limited disruptions. In this paper, the relationship between the maximum allowable duration of Denial-of-Service (DoS) attacks, namely attacks that disrupt both sensor to controller (S–C) and controller to actuator (C–A) communication channels, and the upper bound of disturbances is deduced at length. Moreover, to achieve robust recursive feasibility and closed-loop stability of the system, we discuss that MPC with state, control and terminal constraints has a certain degree of inherent attack tolerance concerning the DoS attack duration and parameters like system matrices and the nominal terminal set. Finally, numerical simulation is given to substantiate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper focuses on the asynchronous filter design based on hidden Markovian model for Markovian jump systems with time-delay and external disturbances. Cyber nonlinearities in the communication environment are also considered. To further reduce the network bandwidth usage, an internal dynamic variable is introduced based on the previously extensively studied (static) event-triggered method, which is called dynamic event-triggered approach. The hidden Markovian model is utilized to characterize the asynchronous phenomenon caused by clock signals out of sync between system and filter mode. Then, according to the augmented filtering error system dynamics, some less conservative sufficient conditions are proposed to guarantee the stochastic stability with H ∞ performance. Consecutively, the filter parameter-solving problem is synthesized by a convex optimization problem. Finally, two numerical simulations are presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper applies variational Bayesian to the stochastic system filtering problem for arbitrary measurement disturbances. Considering the system's robustness when confronted with disturbances, we model its dynamics with a random walk model. Afterward, by assuming an inverse-Gamma distribution, the newly introduced covariance is estimated under a variational Bayesian (VB) framework. The proposed filter improves robustness against additive disturbance and degrades automatically into a Kalman filter when without disturbance. Moreover, this filter avoids the labor-intensive process of tuning parameters. A numerical simulation and an on-site vehicle navigation experiment are given to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

This paper systematically investigates consensus tracking of linear multi-agent systems with disturbances over directed switching topologies. Both state and output feedback H ∞ controllers are jointly synthesized with the triggering mechanism. First, a state-based event-triggered H ∞ controller design for achieving consensus tracking is carried out, which can result in settling the occurrence of disturbances and the reduction of controller updates as well as averting Zeno behavior. Considering the case that only output information of agent is required, an event-triggered H ∞ output feedback controller is then proposed and sufficient condition for the solvability of consensus tracking problem are also derived. Compared with the existing results, the effects of disturbances on directed switching topologies can be tackled when continuous communication and full-state information of the agent are not needed. Numerical simulation are finally conducted to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper studies the preparation of quantum gates that enable the realization of maximum entangled states, i.e., Bell states. A series of unitary time-evolution operators are derived to realize the Bell states through the Hadamard and CNOT gates. These operators are decomposed based on the Cartan decomposition method and steered by control laws designed based on the Lyapunov control theorem. The numerical simulations show that by adjusting proper control parameters in the designed Lyapunov-based control laws, the Hadamard and CNOT gates can be faster and better prepared through the decomposed operators, and more stable and accelerated Bell states can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, we propose two state feedback strategies to exponentially stabilize eigenstates for quantum spin- 1 2 systems based on the state feedback designed in Liang et al. (2018)[1] and Wen et al. (2022)[2], respectively. In order to obtain faster state convergence, we improve the state feedback by increasing the real-time state convergence rate, and prove the exponential convergence. On this basis, we present the way of achieving global exponential stabilization of quantum spin- 1 2 systems under the improved state feedback with the help of noise-assisted feedback. Besides, we redesign the division of state space to improve the state convergence rate further, and compare the state convergence rates of all state feedback strategies that can exponentially stabilize eigenstates for quantum spin- 1 2 systems. The effectiveness and the superiority of the improved feedback control are also verified in numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic SIRS epidemic model with general incidence rate under regime switching is investigated. First of all, by applying the Markov semigroups theory, we establish sufficient condition R T 0 * > 0 for the densities of the distributions of solution converge in L 1 to an invariant density. Then, we prove the extinction of disease under the condition of R T 0 * < 0. Finally, numerical simulations for two special cases of response function are carried out to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time dynamic event-triggered consensus of second-order multi-agent systems with mismatched disturbances is studied. First, a nonlinear finite-time disturbance observer is designed to estimate the mismatched and matched disturbance of each follower. Second, the nonlinear dynamic integral sliding surface is developed through the mismatched and matched disturbance estimated by the observer. Then, combined with the dynamic event trigger strategy and the designed integral sliding surface, a new distributed control protocol is proposed, ensuring the finite-time consensus of the system. An analysis of Zeno behavior has found that the event-triggered interval has a positive lower bound. Finally, the effectiveness of the proposed control algorithm is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper, an observer with exponential time-varying gain is proposed for a class of nonlinear systems in Takagi–Sugeno form with both continuous-time and aperiodic sampled outputs. The proposed observer consists of two parts: one related to the continuous-time outputs and one related to the discrete outputs. Sufficient conditions involving a tuning parameter and the maximum allowed sampling time are derived in terms of quasi linear matrix inequality with respect to a scalar to ensure asymptotic convergence of the observer on the basis of Lyapunov–Krasovskii functional and H ∞ approach. Numerical simulations are provided to show the benefits of the proposed observer. Some comparisons with the high gain continuous-discrete observer and fuzzy observer with constant gain are provided to illustrate the interest of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper studies the distributed optimal coordinated control problem for Euler–Lagrange multi-agent systems with connectivity preservation. The aim is to force agents to achieve the optimal solution minimizing the sum of the local objective functions while guaranteeing the connectivity of the communication graph. For practical purposes, the gradient vector of the local objective function is allowed to use only at the real-time generalized position instead of at the auxiliary system state. To make the control parameters independent of the global information and guarantee the fully distributed manner of controller, the adaptive control is introduced to update the coupling weights of the relative states among neighbors. Moreover, to reduce the resource for control updates, the event-driven communication is employed for the updates of both the relative states and the gradient of the connectivity-preserving potential function. Based on the Lyapunov analysis framework, it is proved that agents can converge to the optimal solution with connectivity preservation and Zeno behavior is excluded for the two event-triggering conditions. Finally, the effectiveness of the proposed method is verified by a numerical simulation example. [ABSTRACT FROM AUTHOR]

In this paper, we consider a malicious attack issue against remote state estimation in cyber-physical systems. Due to the limited energy, the sensor adopts an acknowledgment-based (ACK-based) online power schedule to improve the remote state estimation. However, the feedback channel will also increase the risk of being attacked. The malicious attacker has the ability to intercept the ACK information and modify the ACK signals (ACKs) from the remote estimator. It could induce the sensor to make poor decisions while maintaining the observed data packet acceptance rate to keep the attacker undetected. To maximize the estimation error, the attacker will select appropriate attack times so that the sensor makes bad decisions. The optimal attack strategy based on the true ACKs and the corrosion ACKs is analytically proposed. The optimal attack time to modify the ACKs is the time when the sensor's tolerance, i.e., the number of consecutive data packet losses allowed, is about to reach the maximum. In addition, such an optimal attack strategy is independent of the system parameters. Numerical simulations are provided to demonstrate the analytical results. [ABSTRACT FROM AUTHOR]

• We study the problem of the target layer state estimation in multilayer complex dynamical networks by using functional observability. • We analyze the relationship between the minimum number of auxiliary nodes in the functional observer design and interlayer connection probability. • The proposed method can achieve the accurate estimation with less placement of observers and fewer computational costs. In large-scale complex dynamical networks, it is significant to estimate the states of target nodes with only a part of measured nodes. Meanwhile, multilayer complex dynamical networks exist widely in society and engineering. Therefore, it has important theoretic meaning and practical value to study the state estimation of target nodes in multilayer complex dynamical networks with limited node measurements. In this paper, with the measurable state information of a portion of nodes in one layer in the multilayer complex dynamical network, the state estimation of target nodes in other layers is studied. First, we build the model of the multilayer complex dynamical network which includes some target nodes and sensor nodes. Second, auxiliary nodes are selected by using the maximum matching principle in graph theory to construct the augmented node set. Third, we discuss the relationship between the minimum number of auxiliary nodes and interlayer connection probability in the multilayer complex dynamical network. Forth, an appropriate functional state observer is designed with limited number of measured nodes according to a typical model-based algorithm. Finally, numerical simulations are given to demonstrate the accuracy of the proposed method. The proposed method can achieve the accurate estimation with less placement of observers and fewer computational costs in the multilayer complex dynamical network. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of leaderless consensus control for the multiagent systems whose nonlinear dynamics satisfying incremental quadratic constraints. A distributed dynamic consensus protocol, decided by communication among neighboring agents, is presented to render nonlinear agent consensus with appropriate coupling weights. Next, an observer-based distributed protocol is considered to ensure consensus of nonlinear system without knowing full state information. Further, extensions to consensus strategies with nonlinear dynamics for the leader-following fashion are also addressed. By comparison to the traditional nonlinear consensus control methodologies, the proposed approach generalizes the Lipschitz nonlinearity as well as the combined nonlinearity of one-sided Lipschitz condition and quadratic inner-boundness condition towards a more generalized type of nonlinearity, which shows us a less conservative result in the Lyapunov proof. Finally, the numerical simulations for six agents are illustrated to show the feasibility and performance of the proposed control protocol with or without the presence of the observer. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic event-based resilient consensus control of the multiple networked Euler-Lagrangian (E-L) systems under the Denial of Service (DoS) attacks is considered. Compared with linear cyber-physical systems, nonlinear networked E-L systems are more complex and closer to actual mechanical systems. For the situation where the topology is a strongly connected directed topology, a controller based on a dynamic event-trigger mechanism is designed to achieve consensus control for the networked E-L system in the absence of DoS attacks. Sufficient conditions are presented, which can guarantee the closed-loop system be stable. Then the resilient consensus problem of event-based controllers under energy-constrained DoS attacks is analyzed. The conditions related to the duration and frequency of DoS attacks are given. Zeno behavior is proved does not exist in the proposed control scheme. Finally, some numerical simulation results are given for verifying the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we study a flocking behavior that may or not appear for Cucker - Smale model with distributed time delays. For the short range communicated Cucker - Smale model, the flocking condition has strong restrictions on initial data. For this case, we mainly consider the non - flocking behavior. By establishing and appropriately estimating an inequality of the position variance such that the second order space moment is unbounded, we drive a sufficient condition for the non - existence of the asymptotic flocking when the time delays satisfy a suitable smallness assumption. Furthermore, we also provide a sufficient condition of asymptotic flocking. Finally, we present numerical simulations to validate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed observer design problem of a discrete-time complex dynamical system with long-rang interactions, where the outputs of the system are measured by a group of agents communicating through a directed graph and each agent can access only a part of ones. By introducing the variant d -path Laplacian matrix to describe the long-rang interactions among the multiple agents, a simple full-order distributed observer is first designed. Then, in order to reduce to the number of integrators in the distributed observer, a reduced-order distributed observer is constructed. Finally, some numerical simulations are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

The identification problem of output-error autoregressive (OEAR) systems with scarce measurements is considered in this paper. In order to overcome the massive absence of outputs, an interval-varying recursive identification algorithm is proposed through changing the sampling interval and skipping the missing outputs. Based on the maximum likelihood principle, a maximum likelihood interval-varying recursive least squares algorithm is proposed. The effectiveness of the proposed algorithm is tested by a numerical simulation example, and an application example about the heading motion control of underwater vehicle. [ABSTRACT FROM AUTHOR]

In this paper, the mean-square exponential synchronization of stochastic multilayer networks with white-noise-based time-varying coupling is investigated via intermittent dynamic periodic event-triggered control. The existence of a dynamic term can reduce the number of event triggers. Furthermore, by introducing periodic sampling mechanism, a minimum inter-execution time is guaranteed to avoid Zeno phenomenon. Additionally, by employing Lyapunov method, graph theory, and stochastic analysis techniques, synchronization criteria for multilayer networks under intermittent dynamic periodic event-triggered control are established. To clarify the process of synchronization of multilayer networks, a brief framework is developed on the basis of Tajan's algorithm. Ultimately, theoretical results are applied into Chua's circuits and corresponding numerical simulations are given to illustrate the effectiveness and feasibility of the results. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered joint adaptive high-gain observer design method is proposed for a class of nonlinear systems that are characterized by an unknown parameter entering the system state equations. The main difficulty in the observer design is that the event-triggered mechanism (ETM) is affected by variable delayed-sampled data and the system's unknown parameter. To overcome this difficulty, a closed-loop output predictor is incorporated into the design of the event-triggered mechanism to compensate for the sampling and the delay affecting the system outputs. To prevent the Zeno phenomenon, and to guarantee the exponential convergence of the observer, an exponential decay factor is considered in the ETM. The effectiveness of our proposed observer is demonstrated through numerical simulations, experiments and performances comparison with previous works in the literature. [ABSTRACT FROM AUTHOR]

The output feedback l 1 control problems of positive Markov jump systems(PMJSs) are studied using event-triggered method in this paper. Firstly, two novel dynamic output event-triggered control strategies(DOETCS) are proposed for continuous-time PMJSs as well as discrete-time PMJSs, which can reduce the event-triggered frequency and save network resources. Then, by constructing multiple linear copositive Lyapunov functions, two sufficient conditions about positivity and stochastic stability with l 1 -gain performance are presented for the continuous-time closed-loop PMJSs and the discrete-time closed-loop PMJSs. Simultaneously, two desired output feedback l 1 controllers and two DOETCS are also co-designed to stabilize the PMJSs in continuous and discrete cases, respectively. All the conditions obtained in this paper are in the form of linear programming(LP). Further more, the influence of parameters in the novel DOETCS on the event-triggered frequency is investigated in detail in the section of numerical simulation. Finally, two numerical examples are provided to verify the effectiveness of the proposed design schemes. [ABSTRACT FROM AUTHOR]

The gradient neural network (GNN) method is a novel approach to solving matrices. Based on this method, this paper improves the gradient neural network (IGNN) model with a better effect. The convergence speed is increased by replacing the X i − 1 (k) matrix in the original gradient neural network with the current matrix X i − 1 (k + 1). According to theoretical study, IGNN is globally convergent to the solution of periodic matrix equations. The computer simulation results further confirm the feasibility and effectiveness of IGNN. [ABSTRACT FROM AUTHOR]

Considering that predator-induced fear effects affect the reproductive ability of the prey, as well as predator-predator interference during predation, a predator-prey model with exponential fear effect and Hassell-Varley functional response is proposed. The effects of fear level and the Hassell-Varley constant on the dynamics of the system are analyzed, and the results show that for predators that form tight groups, appropriately increasing the fear level can stabilize the system, while for other predators that do not form tight groups, the effect of fear on equilibrium stability is not particularly obvious. Besides, in order to reduce the negative impact of prey population on the environment, a feedback control strategy is applied to the system, and the existence and stability of order- n periodic solution are investigated by using the method of successive function, Poincaré map and Analogue to Poincaré criterion respectively. The periodic solution not only provides the conditions for control measures to be implemented, but also ensures that the prey population after control does not exceed a given level. To verify the correctness of the theoretical results obtained in this paper, numerical simulations for both models are carried out in MATLAB programs. [ABSTRACT FROM AUTHOR]

This paper investigates the fault detectability issue for linear systems within a set-based framework. A set-based fault detectability analysis method is developed based on the geometric approach-based fault detection observer and zonotopes. Three concepts of fault detectability are established according to the relationship between the observer output sets in the presence and absence of faults. Necessary and sufficient conditions are derived corresponding to different detectability concepts. It can be found that the obtained conditions are related to system inputs in open-loop systems and system controllers in closed-loop systems. The impact of both system inputs and system controllers on fault detectability is analyzed. A numerical simulation is presented to illustrate the validity of the conclusions. [ABSTRACT FROM AUTHOR]

This paper considers the robust output regulation and output constraints of the flexible wing system, under an output disturbance, distributed disturbances, boundary disturbances and a reference. The measurable outputs are merely the in-domain tracking error and the in-domain displacement perturbed by an unknown output disturbance that is used to define the uncertain output constraint. All the above disturbances and reference are supposed to be from an exosystem with unknown initial values. Under the guideline of internal model principle, a PDE-ODE coupled observer is designed to exponentially estimate the states of the wing system and exosystem, and meanwhile guarantee the robustness to the unknown coefficients of the disturbances, reference and output constraint. Observer-based nonlinear controllers are then proposed for the flexible wing system so that the in-domain tracking error is regulated toward zero. Furthermore, the approximately known endpoint tracking errors are further restrained by the estimation of the constraint. A numerical simulation is used to indicate the effectiveness of the designed observer-based dynamic controls. This paper also presents the advantages over the adaptive control and active disturbance rejection control. • A PDE-ODE coupled observer is designed to exponentially estimate the states of the wing system and exosystem, and further gives the approximate values of the endpoint tracking errors and output constraint. • Observer-based nonlinear controllers are proposed to guarantee the robust ourpur regulation of the in-domain tracking error of the flexible wing system. • The approximately known endpoint tracking errors are further restrained by the estimation of the unknwon constraint, which is further proved to be robust to unknown disturbances and references. [ABSTRACT FROM AUTHOR]

As an important technology to improve network reliability, fault diagnosis has gained wide attention in complex dynamical networks. However, few studies focused on detecting the structure of broken edges when faults occur. In this paper, due to the natural sparsity of complex dynamical networks, a completely data-driven method based on compressive sensing is established to detect the structure of faulty edges from limited measurements. The least absolute shrinkage and selection operator algorithm is applied to solve the reconstruction problem. In addition, the method is also applicable to multilayer networks. The faulty edges in both the intralayer network and the interlayer network can be fully identified. Compared with other methods, the main advantages of the proposed method lie in two aspects. First, the structure of faulty edges can be obtained directly with limited measurements. Second, the proposed method is less time consuming and more efficient due to less data processing. Numerical simulations involving single-layer, multilayer and real-world complex dynamical networks are given to demonstrate the accuracy of detecting the structure of faulty edges from the proposed method. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) impact time and angle guidance problem is of great practical significance but remains open because of the coupling nonlinearity and multiple constraints. To solve this problem, a 3D vector guidance law is proposed in this paper to intercept a non-maneuvering target at the desired impact conditions. First, a 3D vector impact angle constrained guidance law with explicit time-to-go estimation is developed by extending the planar one into the 3D space. Then, the intercepting component of the above guidance law is augmented by a time-to-go feedback term, which leads to the proposed 3D vector impact time and angle guidance law. Stability analysis and parameter selection criteria are presented to show the advantageous features of the proposed design. In particular, the proposed guidance law does not require the switch logic, numerical algorithms, or decoupling strategy, which outperforms similar existing results in terms of continuous command and convenient implementation. Finally, several numerical simulations are performed to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

This paper is concerned with the secure bipartite consensus of second-order multi-agent systems under denial-of-service (DoS) attacks. The communication network is an antagonistic network, in which there is cooperative or competitive relationship between neighboring agents. Meanwhile, information cannot be transmitted when the system is attacked. A novel event-triggered control algorithm based on sampled data is proposed to save limited resources and exclude the Zeno behavior. By applying the convergence of monotone sequences, graph theory as well as the discrete-time Lyapunov function method, some sufficient conditions on threshold parameters, frequency and duration of DoS attacks, and sampling period are derived to ensure the bipartite consensus under DoS attacks. Finally, the correctness and advantages of theoretical results are demonstrated by a numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper, considering the influence of multiple delayed output items on the prediction accuracy of echo state network, a novel echo state network with multiple delayed outputs (MDO-ESN) is proposed for time series prediction with multiple delayed outputs. Firstly, for a given learning task, through studying the autocorrelation of output signal, its delayed characteristics can be determined, and then the corresponding delayed item of output equation of the MDO-ESN can be adjusted adaptively. Secondly, in order to improve the adaptability of the MDO-ESN in different learning tasks, a sufficient condition is given to satisfy the stability of the MDO-ESN. Thirdly, a parameter optimization method is given to reduce the dependence of the prediction accuracy of the MDO-ESN on the reservoir parameters of the MDO-ESN. Finally, two numerical simulation examples and one actual simulation example are used for verifying the effectiveness of the MDO-ESN. [ABSTRACT FROM AUTHOR]

This paper proposes a high-precision three-dimensional nonlinear optimal computational guidance law in the terminal phase of an interceptor that ensures near-zero miss-distance as well as the desired impact angle. Additionally, it achieves these ambitious objectives while ensuring that the lead angle and lateral acceleration constraints are not violated throughout its trajectory. This ensures (i) the target does not escape the field of view of its seeker at any point in time (a state constraint) and (ii) it does not demand unreasonable lateral acceleration that cannot be generated (a control constraint). The guidance problem is formulated and solved using newly proposed Path-constrained Model Predictive Static Programming (PC-MPSP) framework. All constraints, both equality and inequality, are equivalently represented as linear constraints in terms of the errors in the control history, thereby reducing the complexity and dimensionality of the problem significantly. Coupled with a quadratic cost function in control, the problem is then reduced to a standard quadratic optimization problem with linear constraints, which is then solved using the computationally efficient interior-point method. Results clearly demonstrate the advantage of the proposed guidance scheme over the conventional Biased PN as well as the recently proposed GENeralized EXplicit (GENEX) guidance techniques. Numerical simulations with variation in initial conditions and Monte–Carlo simulations with parametric uncertainty demonstrate the robustness of the proposed guidance scheme. [ABSTRACT FROM AUTHOR]

This paper studies the flocking control problem of heterogenous multi-agent systems with multi-group tracking various virtual leaders. In particular, hybrid impulsive control protocol is designed based on the partially discrete agents' information transmission, where full information exchange only occurs at each impulsive time instant. Meanwhile, braking and gyroscopic forces are implemented for the collision avoidance purpose. Conditions for multi-group formation are established in terms of coupling strength and the length of impulse period. The designed control protocols guarantee that the multi-agent systems are able to achieve asymptotic stability as well as collision-free motions. Numerical examples and computer simulations are provided to validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies practical consensus problems for continuous-time multiagent systems with heterophilous dynamics. Agents in the network tend to cease communication when they are close enough to each other inspired by the heterophily principle in social and opinion dynamics. We propose two types of state constraints on dynamical agents, in which the hard constraint refers to exogenous restrictions that rule the entire evolution of the agent behavior and the soft constraint turns out to be endogenous and only the ultimate equilibria of the agents are restricted. Sufficient conditions have been established to achieve practical consensus for both types of constrained systems. Numerical simulations show that the practical consensus time grows logarithmically, revealing a rapid convergence with respect to disagreement of initial conditions. [ABSTRACT FROM AUTHOR]

In this paper, the consensus tracking problem is studied for a group of nonlinear heterogeneous multiagent systems with asymmetric state constraints and input delays. Different from the existing works, both input delays and asymmetric state constraints are assumed to be nonuniform and time-varying. By introducing a nonlinear mapping to handle the problem caused by state constraints, not only the feasibility condition is removed, but also the restriction on the constraint boundary functions is relaxed. The time-varying input delays are compensated by developing an auxiliary system. Furthermore, by utilizing the dynamic surface control method, neural network technology and the designed finite-time observer, the distributed adaptive control scheme is developed, which can achieve the synchronization between the followers' output and the leader without the violation of full-state constraints. Finally, a numerical simulation is provided to verify the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

In most existing studies, formation tracking algorithms are generally based on state feedback strategies and assume that agents are treated as points. However, in practical scenarios, directly obtaining the state of the target can often be challenging, and agents may have their own shapes. In this paper, we model agents as rectangles, allowing each agent to establish a safety zone to avoid collisions during formation. Based on these rectangular agents, we design formation tracking algorithms for a mobile target using only local output information. Specifically, two layers of control algorithms are designed for each agent. (1) Upper-level algorithm: two types of fully distributed mobile observers are constructed based on only a portion of the target's output information. This is achieved by applying node-based adaptive coupling gains and edge-based adaptive gains to the consensus-based term in the state estimation algorithm, respectively. (2) Lower-level algorithm: a leader-following formation tracking algorithm is constructed. By cascading these two layers of algorithms, all agents track the target in formation at the same speed as the target. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

Practical time-varying formation-containment tracking (TVFCT) issue for nonlinear multi-agent systems with general noise is researched in this paper. A neuro-adaptive practical TVFCT protocol is put forward, where neural networks (NNs) and parameter adaptive laws are developed for estimating the nonlinearities and compensating the upper bound of the time-varying formation (TVF) condition, respectively. Leaders are expected to achieve the predetermined TVF and followers are driven to move into the formation structure confined by leaders simultaneously. The whole formation position depends on the desired trajectory produced by a virtual-leader. General noise rather than white noise is investigated in the multi-agent systems. The noise-to-state-practically stability in probability (NSPS-P) criteria can be guaranteed by employing the Lyapunov theories combined random differential equations (RDEs). Numerical simulation results reveal the effectiveness of the developed protocols. [ABSTRACT FROM AUTHOR]

From the micro-level perspective of individuals, this paper proposes a node-based SIRS epidemic model on two-layer networks, and investigates the stability of the equilibrium in the model as well as the influence of network structures and infected or recovered rate on disease propagation. By using the stability theory, we give the accurate sufficient condition for the stability as well as the threshold of disease prevalence, showing the threshold of such two-layer network is less than that of isolated single-layer network. Also, an easy-to-use approximate criterion for stability of disease-free equilibrium is provided for the two-layer network with general intra-layer structures when the infected rate across layers is far less than that within layers. Further, extensive numerical simulations on several classical network structure models verify the theoretical results, and show some new interesting results: (1) for the duplex network with equivalent number of connections each layer, the infected rate across layers has more significant effect on epidemic spreading than that within layers; (2) when fixing the whole network edge density, the same number of connections each layer always be the most conductive to propagate of the disease for the duplex network of ER structures, but it is the most/worst conductive to propagate at large/small infected rate for the duplex network of small-world structures. [ABSTRACT FROM AUTHOR]

• A tan-type barrier Lyapunov function is introduced to ensure the prescribed performance of formation errors, the effectiveness of communication connections, and the avoidance of collisions. • A high-gain observer is designed to reconstruct the leader's velocity, which makes the control strategy more versatile in engineering applications. • An adaptive self-structuring neural network is proposed to compensate for model uncertainties and external disturbances, which can optimize the structure of the NNs by adjusting the number of neurons online, thus effectively reducing the computational burden. • A finite-time formation tracking controller for underactuated multiple USVs with error constraints is proposed to guarantee all signals of the closed-loop system to be practically finite-time stable. In this paper, a leader-follower formation control scheme of multiple underactuated surface vessels (USVs) is proposed for trajectory tracking, which not only solves the line of sight (LOS) and angle tracking errors within the prescribed performance, but also avoids collisions and maintains the communication connection distance. To achieve the prescribed performance and converge the tracking errors in finite time, a tan-type barrier Lyapunov function (TBLF) is introduced into the designed control strategy. In the process of formation control design, the measured values of the LOS range and angle are available, and the velocity of the leader is estimated using a high-gain observer. Next, a novel self-structuring neural network (SNN) is proposed to estimate the uncertain dynamics induced by the model uncertainties and environmental disturbances, and the computation amount is reduced by optimizing the number of neurons. Combining coordinate transformation and dynamic surface control (DSC), an adaptive NN controller with prescribed performance is proposed. The Lyapunov analysis shows that, although uncertain dynamics exist, the tracking errors can converge to a small region in finite time while achieving the prescribed performance, avoiding collisions, and maintaining the communication distance. In the closed-loop system, all signals are practical finite-time stable (PFS). Finally, the effectiveness of the proposed scheme is illustrated through a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper deals with the privacy-preserving average consensus problem for continuous-time multi-agent network systems (MANSs) based on the event-triggered strategy. A novel event-triggered privacy-preserving consensus algorithm is designed to achieve the average consensus of MANSs while avoiding the disclosure of the agents' initial states. Different from the approaches incorporating stochastic noises, an output mask function in the proposed algorithm is developed to make initial state of each agent indiscernible by the others. Particularly, under the output mask function, all agents can exactly tend to the average value of initial states rather than the mean square value. Under the proposed algorithm, detailed theoretical proof about average consensus and privacy of the MANSs are conducted. Moreover, the proposed algorithm is extended to nonlinear continuous-time MANSs, and the corresponding results are also derived. A numerical simulation eventually is performed to demonstrate the validity of our results. [ABSTRACT FROM AUTHOR]

In this paper, we study the cooperation problem over a group of discrete-time nonlinear dynamically decoupled multi-agent systems (MAS). A distributed model predictive control (DMPC) scheme is proposed in the event-triggered context. Agents cooperate through a coupled cost function subject to input constraints. From the practical perspective, the additive disturbances are taken into account in the controller design. Using the contraction theory in the framework of Riemannian manifolds, a novel constraint is constructed in the DMPC optimization problem to provide the capability of disturbance rejection. Moreover, the event-triggered mechanism is introduced for saving computational and communicational resources. The event-triggering condition is developed by checking the Riemannian distance between the actual and optimal state trajectories. The stability of the closed-loop system and recursive feasibility of the DMPC scheme, thereafter, are rigorously analyzed. In particular, the stability analysis is built upon the contraction theory, which distinguishes this work from the existing results using the conventional Lyapunov theory. It is shown that the recursive feasibility is guaranteed if the additive disturbances are bounded and the event-triggering condition is properly designed. The numerical simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the smallest asymptotic bound of solutions to a positive mixed fractional-order inhomogeneous linear system subject to time-varying delays and bounded disturbance. In particular, when its linear part is asymptotically stable and the disturbance is small, we have obtained the smallest bound (as the time is large enough) of the solutions with the arbitrary initial condition. In the case where the assumption that the disturbance converges to 0 as the time t tends to infinity is added, we have shown that every solution of the system tends to the origin. Numerical simulations are finally presented to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

• A complete framework for practical application of AV systems is proposed based on a novel scheme of Varying Zonotopic TRMPC with Switching Logic (SVTMPC). • The new update mechanism of the present nominal state can determine the unknown internal disturbance set. • The novel flexible tube parameterization increases QP feasible region and leads to less conservative results. • The switching logic can reduce the conservatism under conventional conditions. • Numerical simulation and HIL experiment verify the superiority of the proposed framework. Model mismatch caused by strong nonlinearity and other factors will severely impact the lateral path tracking control in Autonomous Vehicles (AVs) under extreme conditions. Previous studies have focused on guaranteeing robust stability under possible uncertainty realizations through Tube-based Robust Model Predictive Control (TRMPC). However, three deficiencies in TRMPC applications are revealed: unknown disturbance set, simple rigid tube, and excessive conservatism. In this paper, a novel scheme named Varying Zonotopic TRMPC with Switching Logic (SVTMPC) is developed to overcome these limitations. Firstly, a zero steady-state error dynamic model is established, and a new update mechanism of the nominal state is devised to determine the unknown internal disturbance set of the AV system. Secondly, zonotopic representation of all defined sets is used to construct the prediction model, as well as a flexible tube with varying cross-sections is naturally designed to overcome excessive conservation and non-solution of Quadratic Programming (QP). Finally, a switching logic between conservative and radical strategies improves tracking performance under conventional conditions without compromising robust stability. Numerical simulation through three scenarios shows that the SVTMPC controller can comprehensively improve robust stability and adaptability compared with MPC and TRMPC. Hardware-in-the-Loop (HIL) experiment verifies the effectiveness and real-time of the SVTMPC controller. [ABSTRACT FROM AUTHOR]

This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to unbounded additive uncertainties. The new methods are developed by formulating the chance constraints into deterministic form, which are treated in analogy with robust constraints, by using the probabilistic reachable set. The first one is the time-varying tube-based stochastic model predictive control algorithm, which is designed by employing the time-varying probabilistic reachable sets as tubes. The second one is the constant tube-based stochastic model predictive control algorithm, which is developed by enforcing a constant tightened constraint in the entire prediction horizon. In addition, the soft constraints are proposed to associate with the state initialization in the algorithms to enhance the feasibility. The algorithm feasibility and closed-loop stability results are provided. The efficacy of the approaches is demonstrated by means of numerical simulations. [ABSTRACT FROM AUTHOR]

• The target is considered to be maneuvering. • The terminal acceleration command is constrained to zero without usage of switching logics. • The virtual look angle is constrained during the interception. • The proposed nonlinear guidance law is proved to be near-optimal. • All-aspect interception can be realized. To realize the terminal acceleration constraint for a bias proportional navigation guidance law without usage of switching logics, this paper proposes a modified bias term and presents a terminal acceleration constrained bias proportional navigation guidance law against maneuvering targets. First, a so-called virtual planar coordinates whose origin is attached to the point mass of the target is built, so that the original maneuvering target is transformed to a virtual stationary target. On this basis, the common structure of bias proportional navigation guidance law is presented. To realize the terminal acceleration constraint, a modified bias term related to the relative distance between target and missile is used to improve the bias proportional navigation guidance law. With the virtual look angle and the line-of-sight angle constrained, the proposed modified bias proportional navigation guidance law can intercept the maneuvering targets in a desired attack angle. Comparisons with the optimal guidance law in the linear system are carried out, and the proposed law is proved to be near-optimal. The numerical simulation results demonstrate the all-aspect interception capability of the proposed law against maneuvering targets. [ABSTRACT FROM AUTHOR]

There is a lot of interest in problems of multivehicle systems (MVSs) in recent years due to their wide range of applications. In this paper, we are closely examining three approaches for dealing with problems of coordination control strategies in (MVSs). First, a distributed control approach is discussed where the controller here is designed using bilinear matrix inequality (BMI) technique. Second, a two-level control approach is provided in which a global controller is used to supervise the overall performance of the (MVSs) by sending corrective signals to the local controllers of the agents. In this approach, there is no direct communication among agents but all of the communication with the global controller. Third, a cloud-based control approach is addressed which benefits of the technological advances in networked control systems theory. Computer simulations are performed on autonomous underwater vehicles (AUV) and comparative studies among the approaches are elucidated throughout the paper. [ABSTRACT FROM AUTHOR]

In this paper, a multi-sensor multi-rate (MSMR) sampling mechanism is introduced in multi-agent systems (MASs), and the output consensus problem for heterogeneous MASs with the MSMR sampling mechanism is investigated. By constructing a dynamic compensator for each agent, a new distributed control strategy that does not depend on the eigenvalue information of the Laplacian matrix is proposed. Under the proposed control strategy, the output consensus problem of heterogeneous MASs can be solved by Lyapunov stability method. Moreover, each agent's controller design does not need continuous information of its neighbors. The proposed method in this paper is also used to deal with the state consensus problem and output consensus problem of homogeneous MASs with the MSMR sampling mechanism, respectively. Finally, two numerical simulation examples are given to illustrate the effectiveness of our results and the advantages of the MSMR sampling mechanism in improving the control performance of the MASs. [ABSTRACT FROM AUTHOR]

This paper investigates the neural adaptive tracking control problem of a class of strict-feedback systems considering asymmetric full-state with input magnitude and rate constraint (MRC). By designing a dual-integral-type actual control law, the MRC on system input is transformed to be the magnitude limitations on the extended states of the original system, so the original system with both state and MRC considerations is converted to be a new system with only full-state constraint. Besides, compared with the traditional symmetric integral barrier Lyapunov function, new asymmetric integral barrier Lyapunov function is introduced to the dynamic surface-based controller design process in this paper for dealing with the asymmetric state constraint problem. It is analyzed that the original system is semi-globally uniformly ultimately bounded, and that the desired multiple constraints are never violated. The effectiveness of the control strategy is shown via numerical simulations. [ABSTRACT FROM AUTHOR]

By integrating the finite time control technique and finite-time disturbance observers together, the finite-time three-dimensional path following control problem for small-scale fixed-wing UAVs subject to external wind disturbances is investigated in this paper. The external wind disturbances are estimated through finite-time disturbance observers and the estimates are then incorporated into the finite-time feedback controller such that a composite control scheme is proposed. Under the proposed control scheme, the closed-loop system possesses not only faster convergence rate but also stronger disturbance rejection ability and better robustness, which is the main contribution of the paper. The effectiveness and superiorities of the proposed composite control scheme are demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Up to now, the synchronization of stochastic delayed coupled systems with Lévy noise (SDCSL) has not been fully studied and remains to be significant and challenging. Therefore, this paper devotes to delay-dependent synchronization issue of SDCSL. Firstly, a novel global Lyapunov functional for SDCSL is given. Then, novel delay-dependent criteria are established to ensure the mean square asymptotical synchronization of SDCSL. Different from previous work about synchronization of SDCSL, the synchronization criteria are dependent on time delays in this paper, which makes our synchronization criteria less conservative than delay-independent ones. It is therefore a breakthrough in synchronization research of SDCSL. Moreover, as an application of the theoretical results, the mean square asymptotical synchronization of time delayed Chua's circuits with Lévy noise is investigated in detail. Eventually, some numerical simulations are shown to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the optimal control problems of switched autonomous linear systems and switched non-autonomous linear systems in discrete-time, where the objective is that of minimizing a linear quadratic performance index function or multiple linear quadratic performance index function. The subsystems switching sequence and the number of switchings are free in this paper. By employing an embedding technique, we embed the origin switched autonomous or non-autonomous linear systems into a larger family of systems. Owing to the embedding technique, the optimal control problem of the origin switched linear systems can be relaxed into a traditional optimal control problem for the larger family systems. We thus derive the optimal switching control law and the optimal control input for the bang-bang type optimal solution of the relaxed optimal control problem simultaneously. The optimal solution of the origin switched optimal control problem can be obtained by the bang-bang type optimal solution of the relaxed optimal control problem. Finally, results of numerical simulation examples are given to demonstrate the remarkable effectiveness and superiority of the developed method. [ABSTRACT FROM AUTHOR]