This paper investigates the problem of asynchronous resilient control problem for a class of Takagi-Sugeno (T-S) fuzzy singular Markov jump cyber-physical systems (SMJCPSs) subject to hybrid cyber attacks. To address the practical constraint of limited communication resources in multi-sensor environments, a novel dynamic memory event-triggered weighted try-once-siscard protocol (DMET-WTODP) is proposed. This protocol intelligently schedules the transmission order of multiple sensors and dynamically determines the datasending instants, thereby effectively alleviating network congestion. Furthermore, an asynchronous resilient controller (ARC) is designed to compensate for the mode asynchrony described by an Hidden Markov Model (HMM), as well as hybrid attacks. A unified framework is established to derive control gains suitable for different HMM scenarios. Based on a DoSattackparameterdependent Lyapunov functional, sufficient conditions ensuring the stochastic admissibility of the closedloop system are derived. Finally, the effectiveness and superiority of the proposed control scheme are verified through two typical examples: a DC motor and a singlelink robotic arm.

In unstructured environment, the robot faces the Precise Peg-in-Hole (PPiH) assembly as a non-cooperative issue. The posture uncertainty of the peg presents challenges in searching and inserting the hole. The purpose of the research is to eliminate the posture deviation between the peg and the hole with the force feedback. In this paper, the posture adjustment is divided into rough and fine processes. Firstly, for the rough adjustment, the force-angle samples from the end-effector are trained using a Multi-Layer Perceptron (MLP) model under peg-hole non-contact. The robot is guided by the MLP and adjusts the peg to roughly compensate for the posture deviation. Then, the robot brings the peg toward and contacts the hole. Secondly, for the fine adjustment, a Fuzzy-Finite Variable Admittance Control (FFVAC) model is established to estimate and adjust posture for different peg-hole contact states accurately. By integrating force information with fuzzy logic, the fuzzy inference system with fuzzy rules is developed based on the peg-hole contact states. According to the contact states, the twin delayed deep deterministic policy gradient (TD3) model finds the optimal admittance control parameters to achieve the surface close fitting of the peg and hole. Finally, comprehensive experiments are conducted under the unknown initial posture of the peg. The results are analysed by comparing with the stated of the arts of the posture adjustment methods regarding adjustment accuracy and operation time. The proposed method quickly reduces the posture deviation angle less than 0.2°, facilitates the following hole search and insertion works.

This article develops a zero-sum game-based optimal estimation compensation scheme for multi-agent systems under denial-of-service (DoS) attacks and false data injection (FDI) attacks. First, a unified observer is developed to observe consensus error under the FDI attack and the DoS attack. Second, a FDI secondary compensator (FDI-SC) for the FDI attack is designed to compensate for the unestimated state of the FDI attack estimated by the unified observer and consensus deviations. Third, an integral reinforcement learning (IRL) algorithm is introduced to address the difficulty of computing the unknown FDI-related states. Additionally, the unified observer and FDI-SC are modeled as participants in a zero-sum game, which is integrated with the DoS attack model within the IRL framework to obtain an optimal solution. Finally, the stability conditions are established based on the Hamilton-Jacobi-Bellman and Lyapunov-Krasovskii methods. The simulation results demonstrate the effectiveness of this method.

This paper investigates a robust trajectory tracking control problem of an underactuated unmanned surface vehicle (USV) subject to external disturbances, model uncertainties, actuator saturation and faults. The underactuated problem is solved by introducing a coordinate transformation method that takes the hand position point as an output, which in turn simplifies the controller design. On that basis, two fault-tolerant super-twisting controllers are proposed to solve two different cases of the problem. The first controller is designed to address lumped disturbances with known boundaries. In order to overcome the disturbances more efficiently, a leakage-type adaptive (LTA) law is employed in the second controller to update the control gains and compensates the disturbances without their prior information about the derivative boundaries. Also, an anti-windup compensator is integrated in the controller design to solve the saturation non-linearity problem by incorporating auxiliary variables. Since the designed control law is continuous, it can effectively attenuate the chattering phenomenon and ensure that the tracking errors converge in finite time. Finally, the simulation is performed to demonstrate the effectiveness and superiority of the presented approach.

This article proposes a novel method to achieve strategy consensus for evolutionary games on networks through degree-based pinning and incentive control. First, for 2-strategy evolutionary games on star networks, the necessary and sufficient condition is proposed to achieve strong strategy consensus only using degree-based pinning control. If this condition is not satisfied, the integration of the incentive control method is explored. This integration yields the minimum value of incentive parameter required to achieve strong strategy consensus. Second, for$ k $-strategy evolutionary games on star networks, the necessary and sufficient condition only using degree-based pinning control and the minimum value of incentive parameter are proposed to achieve strong strategy consensus. Third, a hierarchical approach for players is employed to extend these results to evolutionary games on general networks. Under the framework of degree-based pinning control, the algorithm for calculating the minimum value of incentive parameter is introduced to make evolutionary games on general networks achieve weak strategy consensus. Finally, two illustrative examples are employed to demonstrate the effectiveness of the obtained results.

This article investigates fixed-time synchronized convergence for disturbed second-order multiagent systems (MASs) in distributed optimization under the zero-gradient-sum (ZGS) scheme. A fixed-time ZGS distributed optimization method via sliding mode is first proposed for the second-order MASs, which avoids local minimization and rejects disturbances. To further achieve time-synchronized convergence, a hierarchical robust optimization method is then introduced. It employs a time-varying function-based local-minimization-free ZGS scheme within a virtual MAS to generate a reference signal that reaches the global cost function's minimizer and a fixed-time synchronized sliding mode tracking controller to drive the original second-order MAS to track this signal. Beyond the capabilities of the first protocol, this method also ensures the time-synchronized convergence of each agent's state components, low conservatism in terms of convergence time bounds, and privacy preservation. Numerical simulations demonstrate the effectiveness of the proposed methods.

This article studies event-triggered nonsingular terminal sliding-mode control (TSMC) for a class of nonlinear systems. First, a static event-triggering mechanism is implemented in the nonsingular TSMC design. It is shown that the sliding variable can reach the quasi-sliding-mode band and the states can converge to a neighborhood of the equilibrium dependent on the threshold of the event-triggering mechanism. Second, by taking advantage of the internal variable, a dynamic event-triggering mechanism is developed for the nonsingular TSMC design. Compared to the static event-triggered nonsingular TSMC, the designed dynamic event-triggered nonsingular TSMC strategy can reduce the number of events while maintaining the same upper bounds of quasi-sliding-mode and steady states. It is further shown that both event-triggered nonsingular TSMC systems have no Zeno behavior. Finally, simulation results are given to demonstrate the effectiveness of the theoretical results.

This article investigates the event-triggered (ET) almost output regulation (ETAOR) issue for the switched T-S fuzzy (T-SF) systems with both output regulation (OR) characteristic and $L_{2}$ gain characteristic considered. First, in order to conserve communication resources, an ET mechanism and an ET switched fuzzy feedback controller are devised. Then, the definition of the ETAOR issue for the switched T-SF systems is presented. Next, with the relaxed assumption of the same coordinate transformation, the ETAOR issue of the switched T-SF systems is transformed into the ET $H_{\infty } $ control problem of the switched T-SF systems. Further, by using the multiple Lyapunov functions approach, a solvability condition on the ETAOR issue is established for the switched T-SF systems with the average dwell-time related switching signals. Such condition is also suitable for nonswitched T-SF systems. In addition, Zeno behavior may be caused by the ET programme is excluded. Finally, the presented control method is applied to an aero-engine case study to corroborate its effectiveness.

This article focuses on the adaptive anti-disturbance bumpless transfer (AADBT) control issue for the switched neural network systems (SNNSs) encountered by the unmeasurable neural network (NN) modeled disturbance and the measurable unmodeled disturbance through the multiple Lyapunov functions method. First, an adaptive regulator is set to appropriate the unknown parameter in the NN disturbance model. Then, an adaptive disturbance observer is built to estimate the NN modeling disturbance. Further, based on the adaptive regulator and disturbance observer, a switched controller and a state-relevant switching regulator are dual designed to compensate the influence of the NN modeled disturbance and alleviate the influence of the unmodeled disturbance with the jumps in the control input are reduced. Finally, the established control scheme is applied to the switched RLC circuit model, exhibiting the effectiveness of the control strategy.

This article develops continuous and periodic event-triggered sliding-mode control (SMC) algorithms for path following of underactuated surface vehicles (USVs). Based on the SMC technology, a continuous path-following control law is designed. The upper bounds of quasi-sliding modes for path following of USVs are established for the first time. Subsequently, both continuous and periodic event-triggered mechanisms are considered and added into the proposed continuous SMC scheme. It is demonstrated that with appropriate selecting of control parameters, the use of hyperbolic tangent functions does not affect the boundary layer of quasi-sliding mode caused by event-triggered mechanisms. The proposed continuous and periodic event-triggered SMC strategies can make the sliding variables reach the quasi-sliding modes and stay in there. Moreover, energy consumption can be reduced. Stability analysis shows that the USV can follow a reference path by using the designed method. The simulation results show the effectiveness of the proposed control methods.

This investigation proposes an event-triggered-based antidisturbance switching control technique for the switched Takagi–Sugeno (T–S) fuzzy systems (FSs) subject to multiple disturbances as well as unavailable system state. The disturbances are comprised of two parts, i.e., the unavailable modeled disturbances and the available unmodeled disturbances. First, a composite observer is constructed to capture the unavailable system state and unavailable modeled disturbances. Then, on the basis of the observer, a switching controller with an event-triggered program inserted is established. Meanwhile, a switching criterion is formulated. Further, under the developed controller and switching criterion, the sufficient conditions are presented for the switched T–S FSs to achieve the multiple disturbances suppression and the communication transmission resource saving. In the end, the reasonability of the raised event-triggered-based antidisturbance switching control scheme is verified with the employed simulation example.

Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture user' preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memory-enhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.

In this note, we investigate the fixed time consensus in probability for stochastic multi-agent systems under undirected graph. First, a nonlinear consensus protocol with Gaussian white noise is given, and the concept of fixed-time consensus in probability is given. Second, we prove that multi-agent systems can achieve fixed-time consensus in probability under undirected topology by using graph theory, stochastic Lyapunov theory and probability theory. Finally, a simulation example is given to verify the effectiveness of the theory results.

In this note, under time varying undirected graph, we discuss fixed-time consensus of multi-agent systems and present one framework for constructing effective distributed protocols, which is continuous and distributed state feedbacks. Based on the theory of fixed-time stability, and we prove that if the sum of time intervals is larger than a given time, and the bounded of initial states is known, the protocol, which is proposed in this note, solves fixed-time consensus problems.

Piezoelectric actuators (PEA) are the fundamental elements for high-precision high-speed positioning/tracking task in many nanotechnology applications. However, the intrinsic hysteresis observed in PEAs has impaired their potential, specially, the motion accuracy. In this paper, the complicated nonlinear dynamics of PEA including hysteresis, creep, drift and time-delay etc. are treated as a black-box system exhibited as rate-dependent hysteresis. The multi-valued hysteresis is analyzed as a single-valued function so that a neural network (NN) can be built to model the hysteresis and its inversion. A sliding mode controller (SMC) augmented with inverse hysteresis model is then developed to compensate the hysteretic behavior, modeling error and disturbance to improve the positioning/tracking stability and accuracy. The effectiveness of this algorithm experimentally verified through the actual tracking control of a PEA.