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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]

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]

• 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 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]

In this paper, the leader-following bipartite consensus is investigated for a group of uncertain multiple Euler–Lagrange systems with disturbances. An innovative adaptive distributed observer is developed without requiring that followers surely acquire the leader's auxiliary state and system matrix. A directed signed network satisfying the principle of structural balance is exploited to describe the interaction among agents. Then a novel bipartite consensus control protocol is proposed to solve the bipartite consensus problem of multiple Euler–Lagrange systems. The theoretical proof is provided via constructing a Lyapunov function and applying Barbalat lemma to analyze the convergence problem. Finally, a numerical simulation is utilized to demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme. [ABSTRACT FROM AUTHOR]

A vibration control strategy is addressed for the three-dimensional vibration suppression of an Euler-Bernoulli beam (EBB) subject to asymmetric output constraints in this paper. A special piecewise barrier Lyapunov function candidate is provided to prevent the asymmetric output constraint violation, and a model-based boundary control (MBC) is developed subsequently to ensure the coupled vibration reduction. Moreover, considering the parameter uncertainties, an adaptive law is designed to estimate the uncertain parameter and to update the vibration controller. The main challenge of this work is to consider the asymmetric output constraint in a nonlinear and coupled infinite-dimensional system. Finally, numerical simulations are made to present the system performance under the proposed vibration controllers which maintain the output being restrained in the predefined scope. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the finite-time robust control problem of a class of nonlinear time-delay systems with general form, and proposes some new delay-independent and delay-dependent conditions on the issue. First, by developing an equivalent form, the paper studies finite-time stabilization problem, and presents some delay-dependent stabilization results by constructing suitable Lyapunov functionals. Then, based on the stabilization results, we study the finite-time robust control problem for the systems, and give a robust control design procedure. Finally, the study of two illustrative examples shows that the results obtained of the paper work well in the finite-time stabilization and robust stabilization for the systems. It is shown that, by using the method in the paper, the obtained results do not contain delay terms, which can avoid solving nonlinear mixed matrix inequalities and reduce effectively computational burden. Moreover, different from existing finite-time results, the paper also presents delay-dependent sufficient conditions on the finite-time control problem for the systems. [ABSTRACT FROM AUTHOR]

In this paper, a nonseparation approach is developed to explore the problem of finite-time and fixed-time synchronization for fully complex-valued dynamical networks. Firstly, to avoid the traditional separation method, the signum function of complex-valued vectors is proposed as an extension of the real-valued signum function and several essential properties of it are derived. Based on the introduced signum function, several complex-valued controllers are directly designed on the networks, which are totally different from the existing real-valued control designs on the decomposed subsystems. Furthermore, by directly constructing Lyapunov functions in the complex field and developing some innovative inequalities, several criteria described by matrix inequalities are derived to achieve finite-time or fixed-time synchronization, which are much simpler and more convenient compared with the previous algebraic conditions. Finally, some numerical simulations are provided to support the validity of the derived results. [ABSTRACT FROM AUTHOR]

• A stochastic epidemic model for cholera is proposed and investigated. • We establish sufficient conditions for extinction of the disease. • We establish sufficient criteria for the existence of a unique ergodic stationary distribution. • The existence of an ergodic stationary distribution implies that all the individuals can be coexistent. In this paper, a stochastic epidemic model for cholera is proposed and investigated. Firstly, we establish sufficient conditions for extinction of the disease. Then we establish sufficient criteria for the existence of a unique ergodic stationary distribution of the positive solutions to the model by constructing a suitable stochastic Lyapunov function. The existence of an ergodic stationary distribution implies that all the individuals can be coexistent in the long run. Finally, some examples together with numerical simulations are introduced to illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the problems of the stability, stabilization and H ∞ control for switched port-controlled Hamiltonian (SPCH) systems. For unforced SPCH systems, via energy-based multiple Lyapunov functions methods, sufficient conditions of both exponential stability and asymptotical stability are obtained under arbitrary switching signals with an average dwell time scheme. Then, based on the state feedback, a switching controller is designed to stabilize SPCH systems, and a switching H ∞ controller is presented to attenuate the external disturbances for SPCH systems, while SPCH systems are subject to actuator saturation, the stabilization and H ∞ control are investigated and the truncation-inequality method is employed to deal with the saturation. Finally, two numerical simulation examples are provided to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract Competitive neural networks(CNNs) has not been well developed in nonlinear fractional order dynamical system, which is developed first time in this paper. Then, by means of a proper Lyapunov functional, asymptotic expansion of Mittag-Leffler function properties, together with some Caputo derivative properties, the testable novel sufficient conditions are given to guarantee the existence, uniqueness of the equilibrium point as well as global asymptotic stability for a class of fractional order competitive neural networks (FOCNNs) are all derived in the form of matrix elements. Furthermore, the boundedness for the solution of FOCNN is presented by employing Cauchy–Schwartz inequality and Gronwall inequality. Besides, a linear feedback control and adaptive feedback control are designed to achieve the global asymptotic synchronization criterion for FOCNNs with time delay and these explored consequences are extended from some previous integer order CNNs output. At last, two numerical simulations are performed to illustrate the effectiveness of our proposed theoretical results. [ABSTRACT FROM AUTHOR]

Highlights • A network-based active suspension model with event-triggering mechanism is established. • A new sufficient condition is developed to design the desired event-triggered controller. • A single-step method is employed such that the problem can be solved by the LMI algorithm. Abstract This paper presents a novel event-triggered H ∞ static output-feedback control for active vehicle suspension systems with network-induced delays. The proposed control schema introduces an event-triggering mechanism in the suspension system such that the communication resources can be significantly saved. By applying some improved slack inequalities and an augmented Lyapunov–Krasovskii functional (LKF), a new design condition expressed in the form of linear matrix inequalities (LMIs) is developed to derive the desired event-triggered controller. The obtained algorithm is then employed to solve the static output-feedback control gain. Compared with the traditional sampled-data H ∞ control scheme, the proposed controller is able to provide an enhanced disturbance attenuation level while saving the control cost. Finally, comparative simulation results are provided to show the performance of the proposed event-triggered controller. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a robust adaptive control strategy for a class of state-constrained uncertain nonlinear systems with prescribed transient and steady-state behavior. The prescribed tracking performance can be characterized by constraints on an output tracking error. Both state and output constraints are achieved by bounding integral barrier Lyapunov functions in the backstepping procedure. A robust adaptive term is designed to compress auxiliary system uncertainties without the knowledge of their bounds. The satisfaction of control constraints and tracking error convergence are verified by theoretical analysis and are illustrated by simulation results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability analysis of time-varying delay systems. Unlike the construction of augmented Lyapunov functional and multiple integral Lyapunov functional, novel three Lyapunov functionals are suggested which are delay product type functions and lead to less conservative results. Based on newly developed Lyapunov functionals, three stability criteria are derived and their superiority is described by three numerical examples. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on two classes of coordinated tracking problems for networked robotic systems, including semi-global coordinated tracking (SCT) problem and global coordinated tracking (GCT) problem. Considering that the dynamics of the networked robotic system can be unattainable, several novel model-free controller-estimator algorithms are proposed to solve the SCT and GCT problems, as well as to reject the input disturbances contained in the system dynamics. By invoking nonsmooth analysis and Lyapunov argument, a number of novel criteria (including sufficient criteria, necessary and sufficient criteria) for semi-global and global asymptotic stability of the presented algorithms are established. Finally, simulation experiments are carried out to verify the theoretical results. [ABSTRACT FROM AUTHOR]

It is well-known that the stability analysis of time-delay systems is a key step to design appropriate controllers and/or filters for those systems. In this paper, the problem of the delay-dependent stability analysis of neutral systems with mixed interval time-varying delays with/without nonlinear perturbations is revisited. Bounded derivatives of the discrete and neutral delays with upper-bounds not limited to be strictly less than one are considered. New stability criteria are developed using the Lyapunov Krasovskii methodology which are expressed in terms of linear matrix inequalities (LMIs). An augmented Lyapunov Krasovskii functional (LKF) utilizing triple integral terms and the descriptor transformation is employed to this aim. In addition, advanced techniques such as Wirtinger-based single and double-integral inequalities, delay decomposition technique combined with the reciprocally convex approach, as well as a few effective free-weighting matrices are employed to achieve less conservative stability conditions. Comprehensive benchmarking numerical examples and simulation studies demonstrate the effectiveness of the proposed stability criteria with respect to some recently published results. The efficacy of the modern integral inequalities are also emphasized against the conventional Jensen׳s inequalities. [ABSTRACT FROM AUTHOR]

In this paper, globally asymptotical synchronization for stochastic memristor-based neural networks with random noise disturbance is investigated. Under the framework of differential inclusions theory and set-valued maps, a state feedback controller and an adaptive updated law are designed by constructing a suitable Lyapunov functional. By using Itô formula and some significant inequality techniques, sufficient conditions for the global synchronization of the stochastic memristor-based neural networks which are more general are obtained. Finally, numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, by proposing a sampled-data feedback control scheme, we investigate the sampled-data control problem for exponential synchronization of complex dynamical systems with coupling delay. Through constructing a novel Lyapunov functional, which takes a full consideration of the information of t − t k and t k + 1 − t , ∀ t ∈ [ t k , t k + 1 ) , a new sufficient synchronization criterion is obtained based on the Lyapunov stability theory. Further, we consider the extended dissipativity analysis problem, which contains the H ∞ performance, passivity performance, dissipativity performance and L 2 − L ∞ performance in a unified framework, and a sufficient condition in terms of strict LMIs is derived. Finally, simulation examples are employed to demonstrate the effectiveness and the reduced conservatism of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust distributed model predictive control (MPC) problem for a class of uncertain discrete-time Markovian jump linear systems (MJLSs), subject to constraints on the inputs and states. Polytopic uncertainties both in system matrices and transition probability matrices of Markov process are taken into consideration. The global system is decomposed into several subsystems, and in this way these subsystems are able to exchange information with each other via internet. Furthermore, by constructing a novel Lyapunov functional, a sufficient condition is derived to guarantee the robust stability in the mean square sense for each subsystem with admissible constraints and uncertainties. In terms of the Cauchy–Schwarz inequality, the constrained problem of minimizing an upper bound on the worst-case infinite horizon cost function is transformed into a convex optimization problem involving linear matrix inequalities (LMIs). By solving a series of LMIs, a novel Jacobi iterative algorithm is proposed to design a distributed mode-dependent state-feedback controller, which ensures the local optimality at each sampling instant. Finally, compared with centralized and decentralized control schemes, two numerical simulation examples are employed to show the effectiveness of the proposed distributed algorithm. [ABSTRACT FROM AUTHOR]