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This article focuses on investigating a dynamic output feedback control issue for uncertain nonlinear networked control systems (NCSs) in the presence of Denial-of-Service (DoS) attacks and sensor failures. By using the low and upper membership functions (LUMFs) of interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, the uncertainties existing in the considered NCSs are depicted effectively. Based on the IT2 fuzzy system, a switching fuzzy model is further established to correspond to different DoS attack scenarios. According to the average dwell-time approach and Lyapunov stability theory, an IT2 fuzzy dynamic output feedback control scheme is proposed so that the exponential stability of the considered NCSs can be achieved regardless of DoS attacks and sensor failures. Lastly, illustrative examples are presented to confirm the theoretical results proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the problems of stability and stabilization of nonlinear discrete-time switched systems is investigated. Firstly, in order to implement the lower bound of minimum average dwell time (ADT) of discrete-time switched nonlinear systems, the ι -open-chain and quasi-cyclic switching signals are introduced. Secondly, the problem of these underlying nonlinear discrete-time switched systems are solved by using the interval type-2 (IT2) fuzzy modeling approach. Thirdly, a novel delayed IT2 fuzzy controller is devised to guarantee the asymptotically stable of the resulting systems. Finally, a numerical simulation example is given to show the merit and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the bipartite consensus tracking control problem with full-state constraints for p-norm multiagent systems. For the full-state constraints problem of p-norm multiagent systems, a transformed function is utilized to achieve the objective of the constraints, which has the property of low complexity because it avoids the intervention of log-type functions or trigonometric functions in the controllers. Meanwhile, the bipartite control performance of p-norm multiagent systems is also guaranteed. Moreover, under the simplified reinforcement learning framework, a compensation strategy is utilized to compensate the unknown ideal weights caused by the simplified reinforcement learning algorithm of critic-actor method, and greatly improve the accuracy of the tracking performance for p-norm multiagent systems. Furthermore, the effectiveness of the proposed strategy is illustrated by an actual simulation. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time fault-tolerant containment control problem for stochastic nonlinear multiagent systems with intermittent actuator faults. A modified distributed observer is designed to estimate unmeasured states in systems. Compared with the existing result, the estimation precision of the modified observer is significantly enhanced in the presence of various uncertainties in the control channel. Moreover, this paper designs a compensation mechanism for intermittent faults to suppress the adverse effects caused by each fault. Then a finite-time fuzzy fault-tolerant containment control scheme is proposed to make all followers converge to the convex hull formed by leaders in a finite time. It is proved that all signals of closed-loop systems are semi-globally finite-time stable in probability based on the finite-time stochastic Lyapunov stability theory, and containment errors can converge to a small neighborhood of the origin. Finally, simulation studies are provided to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the fixed-time fuzzy adaptive self-triggered control of n -link robotic manipulators. To achieve fixed-time stability, a fuzzy adaptive fixed-time control algorithm is proposed by reconstructing the self-triggered controller and using the singularity-free function, which avoids the singularity problem and obtains fixed-time stability. Meanwhile, the self-triggered mechanism reduces the communication resources between the actuator and the controller, improving the system's transmission efficiency. Through the above thoughts, a kind of fixed-time fuzzy adaptive controller based on the self-triggered mechanism is designed, which guarantees that the tracking error of each joint converges to the prescribed range within a fixed time. Finally, the effectiveness of the proposed algorithm is verified by a simulation example of the two-link robotic manipulator. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive distributed event-triggered fault-tolerant consensus problem for a class of multi-agent systems with time delays and external disturbance. The communication faults between the agent and its neighbours are considered, and a fault-tolerant control mechanism is designed to overcome the communication faults. Moreover, a distributed event-triggered mechanism is constructed based on the distributed errors, and hence the communication burden can be reduced. Combining the Lyapunov stability theorem with the Lyapunov-Krasovskii function, it verifies that the consensus tracking errors are bounded with the guaranteed security performance, and the time delays phenomenon can also be compensated. Finally, the effectiveness and utility of the proposed control method are fully validated by two simulation examples. [ABSTRACT FROM AUTHOR]

This paper investigates the fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems (MASs) with dead-zone output. First, by proposing a dead-zone output approximation model and utilizing Nussbaum-type functions, an adaptive compensation mechanism is developed to alleviate the effect of unknown dead-zone output. Meanwhile, the fuzzy logic systems are incorporated to approximate the unknown functions of the nonlinear MASs. Next, by adopting a time-varying scaling function, a consensus control method is recursively established to steer the consensus errors into a small range within the prescribed time. Furthermore, a self-triggered scheme is established to conserve communication resources while avoiding continuously monitoring trigger conditions. It is illustrated that boundedness of all signals and practical prescribed-time leader-following consensus can be achieved. Finally, the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of event-triggered fuzzy adaptive quantized control for stochastic nonlinear non-affine pure-feedback multi-agent systems. The fuzzy logic system is used to estimate the stochastic disturbance term and unknown nonlinear functions. A nonlinearity decomposition method of asymmetric hysteresis quantizer is proposed by applying sector bound property. Moreover, to reduce the communication burden, an adaptive event-triggered protocol with a varying threshold is constructed. Based on the backstepping technique and stochastic Lyapunov function method, a novel adaptive event-triggered fuzzy control protocol and adaptive laws are constructed. By using stochastic Lyapunov stability theory, it is demonstrated that all signals are bounded in the closed-loop systems in probability and all the outputs of followers converge to the neighborhood of the leader output. Simulation results illustrate the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

This paper studies the fuzzy tracking control problem of nonlinear multi-agent systems with time-varying actuator faults and unknown control directions. The term of time-varying actuator faults is estimated via designing adaptive parameter. For the problem of the unknown control directions, the Nussbaum function is introduced to ensure the controller is unaffected on unknown direction. Moreover, based on algebraic graph theory and Lyapunov stability theory, a new distributed controller is designed to guarantee that the tracking error converges to a small neighborhood near the origin. Finally, simulation examples are given to verify the theoretical results. [ABSTRACT FROM AUTHOR]

We experimentally investigate gas pressure-dependent effect on the high harmonic generation (HHG) of Ar and N 2 gases in a tight focusing regime with near IR ultrafast laser fields. Different from a loose-focusing condition, the measured data show a shift of the pressure regime for phase matching towards to the lower pressure side with the increasing driven laser intensity. By comparing the results with a theoretical calculation based on the Constant’ one-dimensional model, we illustrate that this pressure dependence stems from the Gaussian beam phase gradient and atomic dipole phase which can become the key factors for phase matching in the tight focusing regime, interested in a high-repetition-rate coherent extreme-ultraviolet generation. [ABSTRACT FROM AUTHOR]

This paper aims to design distributed consensus protocols which satisfy two design requirements for identical general linear multi-agent systems on fixed, undirected graphs: meeting the global optimality and guaranteeing a prescribed convergence speed. By using inverse optimal approaches, the optimal partial stabilization is developed and the globally optimal distributed consensus problem for leader following and leaderless problems are solved. To obtain prescribed convergence speed of the multi-agent system, novel globally optimal distributed consensus design procedures are proposed. First, combining with the regional pole assignment, the optimal control can be found by solving a strict linear matrix inequality (LMI) problem. It turns out that the increasing number of the agent nodes will not increase the computational complexity. Then, a modified linear quadratic regulator (MLQR) design method is developed which leads to a model free design procedure by employing the adaptive dynamic programming (ADP) technique. Finally, a numerical example is given to illustrate the effectiveness of the proposed procedures. [ABSTRACT FROM AUTHOR]

The event-triggered fault detection problem for nonlinear semi-Markov jump systems constructed by Takagi–Sugeno fuzzy approach is studied in this paper. Owing to that the premise variables and the modes are usually mismatched for both the filter and the plant in actual network environment, i.e., the double asynchronous phenomenon, a novel double asynchronous fault detection filter is designed to take the place of the traditional filter and tackle the fault detection problem for fuzzy semi-Markov jump systems. Particularly, the event-triggered scheme is considered to reduce the waste of communication resources, which gives rise to the premise variables asynchronous phenomenon for the plant and the filter. The modes information of the fuzzy semi-Markov jump systems is not always instantaneously accessible, which leads to the mode asynchronous phenomenon for the plant and the filter. This mode asynchronous phenomenon is described by a hidden semi-Markov model. Based on Lyapunov stability theory, the sufficient conditions are developed to ensure that the system not only is stochastically stable and meets an H ∞ performance, but also can make sure that the filter gains could be captured. Finally, an illustrative tunnel diode circuit example is provided to show the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

• IT2 fuzzy is first developed to hybrid grid-connected generation and it can represent more universal nonlinear systems for region fuzzy membership functions in comparison to subsistent literatures on type-2 fuzzy or T-S fuzzy. • The paper designs an IT2 fuzzy controller which can actually add substantially to the stability of the system, enhancing robustness to disturbance and dynamic performance in dealing with time delays. • We first apply switched model to simulate hybrid grid-connected generation system and it can approach the physical truth well, which makes the theoretical research with more practical significance. • The superiority of switched IT2 fuzzy control in ease of successfully coordinating the individual components of the hybrid grid-connected generation system and operating in a dynamically stable way subordinating to the given patterns. The paper concentrates on H ∞ control problem of switched IT2 fuzzy nonlinear systems with multiple time delays and disturbance. The problem exists extensively in hybrid grid-connected generation converter system, which can be felicitously described through switched interval type-2 (IT2) fuzzy model for bifurcation behavior, electromagnetic interference. The application of IT2 fuzzy switched descriptor in hybrid grid-connected generation is proposed for the first time avoiding bifurcation behavior effectively and successfully coordinates the individual components of the hybrid grid-connected generation system leading to operating in a dynamically stable way subordinating to the given patterns. To ensure the system stable subjecting to disturbance, methods of LMI and free-weighting matrices are employed to design the controller. Availability and validity are verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

• The DSC technique is employed to deal with the "complexity explosion" problem for state-constrained non-affine nonlinear systems subject to output dead zone and input saturation, and the robust compensator is designed to reduce the influences of disturbances and uncertainties. • The unknown control coefficient derived from output nonlinearity is handled through adopting the property of Nussbaum function. • A series of barrier Lyapunov functions are designed, which effectively guarantees that prescriptive ranges of system states cannot be violated. This paper disposes of the problem of adaptive dynamic surface control for non-affine nonlinear systems. The full state constraints, output dead zone and input saturation are fully considered in the controlled system. Invariant sets are used for a continuous and semi-bounded condition of non-affine functions. The "complexity explosion" issue caused by backstepping procedure is avoided via the dynamic surface control method. A Nussbaum function is used to handle the unknown control coefficient derived from the output dead zone, and barrier Lyapunov functions are employed to handle full state constraints. To counteract the effects of disturbance and uncertainty, the robust compensator is constructed. In addition, it is proved that all signals in the system are bounded and all states satisfy their constraints. Finally, the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A practical event-triggered design method is developed for cooperative robust containment control problem of multi-agent systems with multiple leaders. The multi-agent systems discussed in this paper are essentially heterogeneous, and each follower is modeled by a integrator with the external disturbance. A containment error also has been expressed to ensure all the outputs of followers converge to the convex hull spanned by the outputs of the leaders. For the case that the undirected communication graph among followers is connected, a distributed containment compensator is designed based on the local state information of neighboring agents. Furthermore, a distributed output feedback controller is designed to solve the event-triggered containment control problem of the continuous-time multi-agent systems. Finally, a numerical example is given to verify the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

The prescribed-time output regulation problem is investigated for a class of uncertain nonlinear multiagent systems (MASs) subject to limited communication resources. To address this challenge, an event-based distributed neuro-adaptive prescribed-time control scheme comprising distributed prescribed-time observers, a dynamic event-triggered mechanism (DETM), and neuro-adaptive prescribed-time controllers is developed. Specifically, a distributed prescribed-time observer is constructed for each agent using event-based communications to estimate the states of the exosystem. The constructed observer operates without requiring prior knowledge of the exosystem dynamics or global information, ensuring that observation errors converge to a small neighborhood around zero within a user-determined time interval. Additionally, the incorporation of the DETM guarantees a positive lower bound on the interexecution intervals, thereby alleviating the communication demands. Building on this observer, neuro-adaptive prescribed-time controllers are derived for each agent, capable of maintaining the system states within a user-defined compact set without the need for prior knowledge of the initial system states. It is demonstrated that the regulated outputs converge to a region arbitrarily tuned by the user within a prescribed time, with all signals remaining bounded and Zeno behavior eliminated. Finally, two examples are exhibited to verify the effectiveness of the obtained results.

A predefined-time adaptive consensus control strategy is developed for a class of multi-agent systems containing unknown nonlinearity. The unknown dynamics and switching topologies are simultaneously considered to adapt to actual scenarios. The time required for tracking error convergence can be easily adjusted using the proposed time-varying decay functions. An efficient method is proposed to determine the expected convergence time. Subsequently, the predefined time is adjustable by regulating the parameters of the time-varying functions (TVFs). The neural network (NN) approximation technique is used to address the issue of unknown nonlinear dynamics through predefined-time consensus control. The Lyapunov stability theory testifies that the predefined-time tracking error signals are bounded and convergent. The feasibility and effectiveness of the proposed predefined-time consensus control scheme are demonstrated through the simulation results.

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step n and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.