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A serious threat to filter design is the difficulty in resisting important‐data‐based (IDB) Denial of Service (DoS) attacks. In this article, a new approach to filter design that is more relaxed and flexible in resisting IDB DoS policies is proposed. The proposed approach introduces the technique of homogeneous polynomials to develop a more general relaxed fuzzy filter design that can reduce conservatism. Moreover, it is guaranteed that the new method will obtain sets of more relaxed filter gains, which are more capable of responding to the IDB DoS policy. This enables the filter to perform more accurate signal estimation and ensures the filtering error system's H∞$$ {H}_{\infty } $$ performance. Finally, the reliability and effectiveness of the proposed filter method are demonstrated by two comparative examples. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization problem of uncertain switched nonlinear systems. Uncertainty and saturated impulses are first considered together in switched nonlinear systems. It appears that the considered systems are robustly locally exponentially stable under the fast average dwell time method and the designed saturated impulsive controller even though subsystems are unstable. At the same time, our conclusions cover some previous results. A step‐by‐step strategy for optimizing multiple objectives is designed to estimate the basin of attraction of the systems as large as possible. At last, the efficacy of our conclusions is illustrated by two given examples. [ABSTRACT FROM AUTHOR]

This article discusses the finite‐time H∞$$ {H}_{\infty } $$ control problem for a type of discrete‐time semi‐Markov jump systems (SMJSs) with partial unavailable information. A mode‐dependent resilient state feedback control strategy and the incomplete semi‐Markov kernel (SMK) method are applied to stabilize the obtained closed‐loop systems to protect against additive disturbances that can easily occur in the controller. By considering the upper and lower thresholds of sojourn time for each mode, the number of jumps of SMJSs is effectively estimated and new finite‐time bounded related criteria are established, which cover the general case of some previous studies and are more practical in depicting systems. Besides, the H∞$$ {H}_{\infty } $$ performance is further discussed and optimization problems are presented to achieve better closed‐loop performance. At last, a simulation example and a practical example are exhibited to reveal the validity of our raised control strategies and theoretical results. [ABSTRACT FROM AUTHOR]

A novel gain‐scheduling fault estimation is developed on the direction of improving algorithm efficiency based on discrete‐time Takagi–Sugeno fuzzy models. Different from the traditional results in which the zero‐order free matrices are applied, a novel free matrix approach is proposed and the higher‐order free matrices are for the first time introduced in designing conditions of the fuzzy gain‐scheduling fault estimation observer. For each working mode, its proprietary properties about current‐time normalized fuzzy weighting functions are made full use and thus the anti‐interference ability of the developed fuzzy gain‐scheduling fault estimation observer is enhanced without any increase of either the consumed time of solving the required designing criterion or the number of gain scalars needed to be stored in random access memory of the computer in real time. It does provide a good chance to obtain equal or better results of fuzzy gain‐scheduling fault estimation with the usage of much less computing resource than before, which is much meaningful to the applications of theoretical methods. Finally, the effectiveness and superiority of our developed free matrix approach are tested and validated by using performance comparisons on the benchmark example. [ABSTRACT FROM AUTHOR]

The problem of enhanced stabilization of discrete-time Takagi–Sugeno fuzzy system is investigated in depth via proposing a new way to employ much more information implied in various time-varying differences of multi-instant normalized fuzzy weighting functions. Benefit from the given comprehensive real-time scheduling model, these time-varying differences on time vertical dimension and two different transverse dimensions are for the first time integrated into fuzzy stabilization and two key alterable weights are also introduced. Consequently, much considerable freedom can be produced and, thus, it brings much less conservative results than the reported ones in recent literature. More importantly, a very simple workable way has been provided to online recognize the specific enabled mode at each sampling instant so that those existing time-consuming online operations in referred literature can be avoided to ease online implementation costs. Indeed, this feature is very beneficial to the practical application of our theoretical results. Finally, the superiority of our developed method over previous ones is validated adequately via two benchmark simulations. [ABSTRACT FROM AUTHOR]

In this article, a multi‐event‐triggered (MET) near‐optimal tracking control algorithm is developed for a discrete‐time (DT) nonlinear non‐zero‐sum game (NZSG). Firstly, the iterative dual heuristic dynamic programming (DHP) approach is utilized to obtain the optimal tracking control set. Subsequently, distinct independent triggering conditions are formulated for control inputs to improve the utilization efficiency of resources and ensure that the independence between them and the closed‐loop system has an excellent control performance. Meanwhile, input‐to‐state stability (ISS) is proven for the MET tracking plant. Additionally, neural networks (NNs) are designed in the iterative DHP algorithm to construct the model module, the critic module and the action modules, with aims to identify the unknown system, approximate costate function and estimate optimal tracking control strategy set, respectively. Finally, two numerical experimental simulations are conducted to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Owing to the intricate output characteristics of proton exchange membrane fuel cells (PEMFCs) and the associated electrochemical reactions, they typically represent a complex system with numerous parameters. Consequently, there is a critical need to develop a more suitable output characteristic model. Thus, we propose the Weighted Neighborhood Trust Grey Wolf Optimizer (WNT-GWO) algorithm to address the problem of parameter identification in PEMFC models. WNT-GWO integrates a neighborhood trust model to enhance information exchange among the grey wolves. Additionally, we refine the grey wolf position update formula through dynamic weighting, facilitating a more comprehensive exploration of the search space and improving convergence speed. These adjustments mitigate the risk of converging into local optima, contributing to a more reliable and robust parameter identification process. The efficacy of the proposed algorithm is validated through 23 benchmark functions, demonstrating its superiority over the standard grey wolf algorithm. Finally, we apply the WNT-GWO algorithm to identify the parameters of PEMFC models. Experimental results reveal that, compared to other algorithms, WNT-GWO exhibits an average convergence speed that is 4.32 times faster, with an average absolute voltage error of 0.315882142, which highlight its superior performance of the WNT-GWO algorithm compared to alternative methodologies. • Proposing a novel method to optimize unknown parameters in proton exchange membrane fuel cells. • Integrating Weighted Neighborhood Trust for enhanced exploration capabilities. • Mitigating the risk of local optima, ensuring robust parameter identification. • Validating the method through two practical cases, demonstrating its superior performance. [ABSTRACT FROM AUTHOR]

This paper proposes an event‐triggered adaptive control algorithm for general continuous‐time systems with unknown system models. Unlike existing articles, the developed method does not require prior determination of the knowledge of system dynamics, and effectively reduces the update frequency of key signals through the introduction of event‐trigger mechanism. Three neural networks (NNs) are designed in the identifier‐critic‐actor (ICA) architecture to learn the optimal control solution online. The unknown system is approximated by the identifier NN, the critic NN is designed to approach the optimal cost function, and the actor NN is designed to approach the optimal controller. Besides, under event‐triggered control, the parameters of critic NN and actor NN as well as control signals are updated only at the trigger time determined by the event‐trigger condition, which reduces effectively the computing burden and communication cost. The stability of event‐trigger control and the convergence of parameters of three NNs are verified via Lyapunov method. Finally, two examples are presented to demonstrate the viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article researches the interval‐driven variable convergence rate robust stability/stabilization of uncertain impulsive stochastic systems. First, a new conception of variable convergence rate stability for uncertain impulsive stochastic systems is presented in conjunction with the idea of generalized pole configurations. Also, a new criterion for variable convergence rate robust stability/stabilization of uncertain impulsive stochastic systems is given, which can reflect and regulate the convergence rate of the system state. In addition, a new algorithm is designed by means of an interval‐driven stabilization method. This algorithm, combined with the variable convergence rate criterion, achieves accurate control of the convergence rate of the state at the operational level. Double instances are proposed to verify the superiority of the method. [ABSTRACT FROM AUTHOR]

This paper investigates a parameter-dependent (PD) functional observer-based finite-time adaptive memory integral sliding mode controller (ISMC) scheme for continuous linear parameter varying (LPV) systems with unknown nonlinear functions. First, a novel functional observer including parameters and parameter rates is proposed, and the observer gains can be directly designed. Different from the previous studies, a memory ISMC scheme with a time-varying delay through the functional observer is used for the first time in LPV systems to improve the performance. Moreover, to fully adopt the model characteristics of LPV systems and the functional observer, an integral PD surface function that contains PD input matrices and a memory parameter is established simultaneously. And then, an adaptive compensator is deduced for eliminating the influence of unknown nonlinear functions, so that the designed memory ISMC can make closed-loop LPV systems stable within a preset finite time. Furthermore, stability criteria with less conservatism for the LPV systems are proposed by memory Lyapunov–Krasovskii functional with measurable parameters. These criteria can guarantee LPV systems are finite-time boundedness with H ∞ performance (FTB- H ∞ ) on the sliding stage, arriving stage and entire finite-time region. Finally, a riderless bicycle is given to display the validity and superiority of the developed approach. [ABSTRACT FROM AUTHOR]

In this paper, the exponential consensus of leaderless and leader-following multi-agent systems with Lipschitz nonlinear dynamics is illustrated with aperiodic sampled-data control using a two-sided loop-based Lyapunov functional (LBLF). Firstly, applying input delay approach to reformulate the resulting sampled-data system as a continuous system with time-varying delay in the control input. A two-sided LBLF which captures the information on sampled-data pattern is constructed and the symmetry of the Laplacian matrix together with Newton–Leibniz formula have been employed to obtain reduced number of decision variables and decreased LMI dimensions for the exponential sampled-data consensus problem. Subsequently, an aperiodic sampled-data controller was designed to simplify and enhance stability conditions for computation and optimization purposes in the proposed approach. Finally, based on the controller design, simulation examples including the power system are proposed to illustrate the theoretical analysis, moreover, a larger sampled-data interval can be acquired by this method than other literature, thereby conserving bandwidth and reducing communication resources. [ABSTRACT FROM AUTHOR]

This paper investigates the H ∞ master–slave synchronization problem for delayed impulsive implicit hybrid neural networks based on memory-state feedback control. By developing a more holistic stochastic impulse-time-dependent Lyapunov–Krasovskii functional and dealing with the nonlinear neuron activation function, the stochastic admissibility and prescribed H ∞ performance index for the synchronization error closed-loop system are achieved. In addition, the desired mode-dependent memory-state feedback synchronization controller is acquired in the form of linear matrix inequalities. The free-weighting matrix technique is adopted to remove the inherent limitation of time-varying delay derivative for the implicit delayed systems, and the derivative of time-varying delay is relaxed enough to be greater than 1. The simulation of genetic regulatory network in bio-economic system is given to verify validity of the derived results. [ABSTRACT FROM AUTHOR]

• In this paper, the finite horizon linear quadratic tracking problem transforms to the finite-horizon linear quadratic regulator problem with the help of the augmented system. • The developed algorithm in this paper solves the time-varying Riccati equations offline and backward-in-time without the system dynamics or any model identification schemes through the defined time-varying Q-function. • Compare with the work in [56], the restrictions on initial state of the developed algorithm in this paper are not required. Besides, optimal tracking control can be achieved in this paper. • Compare with the work in [57], the discrete-time system is studied in this paper. While, continuous-time system is considered in [57]. Besides, there is no restriction on the initial value function of the developed algorithm in this paper, while the initial value function of the developed algorithm in [57] needs to meet certain conditions. Conventionally, the finite-horizon linear quadratic tracking (FHLQT) problem relies on solving the time-varying Riccati equations and the time-varying non-causal difference equations as the system dynamics is known. In this paper, with unknown system dynamics being considered, a Q -function-based model-free method is developed to solve the FHLQT problem. First, an augmented system consisting of the controlled system and the desired trajectory system is formulated, and the FHLQT problem transforms to the finite-horizon linear quadratic regulator (FHLQR) problem with the augmented system. Then, a time-varying Q -function which depends explicitly on the control input is defined. With the defined time-varying Q -function, a model-free finite-horizon control method is developed to approximate the solutions of the time-varying Riccati equations of the transformed FHLQR problem. At last, simulation studies are carried out to verify the validity of the developed method. [ABSTRACT FROM AUTHOR]

This paper proposes an H ∞ / H ∞ optimization technique to distributed fault detection (FD) for multi-agent systems (MASs). To simultaneously consider the effects of unknown disturbances and faults in the MASs and event-triggered transmission errors (ETTEs) on the residual signal, a residual generator related to the event-triggered threshold coefficient is designed. The generated residuals achieve the best compromise between robustness to unknown disturbances and sensitivity to faults. The obtained results have a better detection performance by using H ∞ / H ∞ optimization technique compared with other optimized schemes. In addition, the applicability of the proposed H ∞ / H ∞ optimized distributed FD scheme is verified by the simulation of a vehicle lateral dynamic system. [ABSTRACT FROM AUTHOR]

• The event-triggered distributed FTC problem of uncertain nonlinear interconnected systems subjected to unmatched interconnections and time-varying actuator faults is solved by integrating the ADP approach and the dynamic event-triggered control strategy for the first time. • A new cost function is introduced to handle the unmatched interconnections and actuator faults simultaneously. • A new dynamic event-triggered mechanism is established, which has the advantage of enlarging sampling interval. Moreover, the restriction on initial admissible control policy for each auxiliary subsystem is obviated by introducing a robust term into the weight learning law of critic NN. • Theoretical analysis shows that the adverse effects of actuator faults on the stability can be eliminated and the cost function of overall interconnected system can be guaranteed to have an upper bound. A novel dynamic event-triggered distributed fault tolerant control (FTC) scheme with guaranteed cost is proposed for uncertain nonlinear interconnected systems suffering from time-varying actuator faults. The event-triggered FTC problem of uncertain interconnected system is first converted to a series of equivalent event-triggered optimal control designs of isolated fault-free auxiliary subsystems by defining a modified cost function for each auxiliary subsystem. The equivalence of problem transformation is demonstrated meanwhile the upper bound of cost function of overall interconnected system is analyzed. Subsequently, for the isolated auxiliary subsystem, a dynamic event-triggered adaptive dynamic programming (DET-ADP) algorithm is proposed to obtain the optimal control policy, which can remove the restriction of initial admissible control, eliminate the actor network, and save more communication resources. Furthermore, we prove that the closed-loop auxiliary subsystems are ensured to be uniform ultimate bounded based on Lyapunov approach. Finally, simulation results are presented to validate the developed dynamic event-triggered distributed guaranteed cost FTC scheme. [ABSTRACT FROM AUTHOR]

• The adaptability against uplink data loss for nonlinear plant is enhanced evidently. • An additional constraint is for the first time proposed for updating the plant status instantly. • A worse health quality of uplink channels can be affordable in this study. The problem of promoting the adaptability to uplink data loss for nonlinear plant is addressed via developing the fuzzy gain-scheduling stabilization law based on a novel online evaluation mechanism. Different from those previous methods, the proposed online evaluation mechanism is dependent not only the current-time but also the past-time normalized fuzzy weighting functions, and thus much more information can be absorbed and utilized in the process of stabilization synthesis by introducing extra free matrices than before. More importantly, an additional constraint is for the first time proposed for updating the plant status instantly so that critical replacement operations can be implemented for reducing the conservativeness of the designing conditions. Therefore, the adaptability to uplink data loss is improved evidently and thus the worse communication quality of uplink channel is bearable in this study. Finally, the advantages relative to those reported ones are tested and validated through benchmark numerical comparisons. [ABSTRACT FROM AUTHOR]

This paper is focused on the security control problem for the dynamic event-triggered networked T-S fuzzy control system with non-uniform sampling and stochastic network attacks. Different from existing research, the signals from sensor are sampled in a non-uniform period. To improve communication, a novel dynamic event-triggered method is proposed, which can adjust the trigger parameters dynamically according to the changes of external output. Then, a T-S fuzzy dynamic output feedback control model with event-triggered mechanism and network attacks is constructed. Furthermore, by introducing a set of slack matrix, the asynchronous premise variable problem caused by event-triggered method is solved, and some sufficient conditions that insure the asymptotic stability with an H ∞ performance requirement of the established closed-loop system can be obtained by utilizing Lyapunov function method and stochastic analysis technique. Moreover, the gains of the fuzzy dynamic output feedback controllers are found by solving the linear matrix inequalities (LMIs). Finally, two practical examples are given to support the merits and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper presents a new Lyapunov-type predefined-time stabilization control algorithm for stochastic high-order nonlinear systems with asymmetric output constraints. In contrast to stochastic finite-time and fixed-time stabilization, the average value of the settling-time function for stochastic predefined-time stabilization control is independent of both the initial value and the control factors. To mitigate the significant uncertainties arising from the asymmetric output constraint, a tan-type barrier Lyapunov function is formulated. Furthermore, by harnessing the previously mentioned barrier Lyapunov function and integrating the power integrator technique, a controller design strategy is formulated based on the backstepping method. The rigorous analysis in this study proves that the designed controller ensures both the attainment of predefined-time convergence of the system states to the origin in probability and the satisfaction of the output constraint. Finally, an example of a roll angle subsystem for quadrotor UAVs and a numerical illustration are presented to corroborate the theoretical analysis. • A novel Lyapunov-type stochastic predefined-time stable control algorithm is presented in Theorem 1. • Controller design based on the backstepping method is formulated through the power integrator technique. • The tan-type barrier Lyapunov function extends the order range and absorbs the inherent properties of the nonlinear terms. [ABSTRACT FROM AUTHOR]

This article centers around the privacy‐preserving‐based secure H∞$$ {H}_{\infty } $$ filtering issue for interval type‐2 (IT‐2) fuzzy networked systems with false data injection (FDI) attacks. In order to achieve the goal of privacy preserving and significantly enhancing system security against potential eavesdropping threats, a novel encryption‐decryption mechanism (EDM) is adopted to safeguard the safety of signals across the network. The mechanism encrypts the transmitted signal by introducing artificial noise, secret key, and utilizing randomly selected nodes. This ensures that the actual transmitted data remains invisible to eavesdroppers while minimally the impact on the estimated performance of the proposed EDM. Given the network communication resources are becoming constrained due to the ever‐increasing network traffic, an adaptive event‐triggered mechanism (AETM) is employed to ease network congestion by an adaptively adjustable threshold. Then, various sufficient conditions have been outlined to ensure that the filtering error system meets the prescribed disturbance attenuation level. In the end, a numerical example is presented to evaluate both the precision and effectiveness of the developed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the security control issue for fuzzy networked systems with uncertainty under false data injection (FDI) and aperiodic denial‐of‐service (ADoS) attacks via sliding mode approach. To mitigate the communication burden, a novel mechanism compromising the advantages of the adaptive event‐triggered (AET) scheme and weighted try‐once‐discard (WTOD) protocol is proposed. In particular, the triggered threshold with real‐time characteristics varies with the measurement outputs. To be specific, the shared insecure communication network can be subjected to hybrid attacks including the stochastic FDI attacks and ADoS attacks limited by duration and frequency. Moreover, the token‐dependent fuzzy non‐fragile observer (NFO) with norm‐bounded uncertainty and corresponding fuzzy control law are constructed by virtue of the Zero‐Order‐Hold (ZOH) strategy. Then, several sufficient criteria that guarantee the exponential stability of the system and the reachability of the specified sliding surface are given. Eventually, a numerical example is presented to verify the applicability of the proposed security control strategy. [ABSTRACT FROM AUTHOR]

The article concentrates on exploring the issue of privacy‐preserving sliding mode consensus of multi‐agent systems (MASs) with disturbance. An encryption and decryption algorithm has been proposed to address data security and privacy issues during data transmission. To optimize network resource allocation, a dynamic event‐triggering mechanism has been introduced, which reduces the number of encrypted data while saving the computation cost. The consensus performance based on the sliding mode control strategy is achieved when the reachability of the slide‐mode surface is guaranteed, and then the slide‐mode controller is developed. Finally, an empirical demonstration through a numerical example validates the efficacy of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this article, we investigate a novel reinforcement-learning (RL)-based scheme to address the optimal leader–follower consensus issue for constrained-input continuous-time multiagent systems. First, as for input-constrained problems, the fundamental smooth assumption is not satisfied for the value function. To deal with this problem, we employ the method of vanishing viscosity solutions to relax this smooth assumption to a continuity assumption for the value function, which broadens the scope of RL applications. Second, the control cost functions take a more general form which guarantees the continuity of the optimal control policy instead of the specific integrand form used in previous input-constrained RL-based schemes. Based on these results, we introduce a novel identifier–critic–actor structure to extend the conventional critic–actor RL framework into a distributed model-free one, where the learning of the identifier, critic, and actor is online and simultaneous. We provide the simulation examples to validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Resource allocation in logistics poses intricate challenges due to its complexity and non-linearity. This paper presents a novel warehouse human resource scheduling model that combines queuing theory and integer programming. We introduce the Adaptive Quantum Differential Evolution (AQDE) algorithm, inspired by quantum computation, to address the optimization complexities. Our approach demonstrates superior global convergence accuracy and speed compared to other DE variants and evolutionary algorithms. The efficacy of AQDE is validated through experiments using real logistics warehouse data, showcasing its efficiency in allocating logistics resources. • Optimization of logistics warehousing staff scheduling has good practical significance. • A multi-objective evolutionary algorithm combined with quantum computing is proposed. • A logistics warehousing scheduling model based on reality is designed. • An improved differential evolution algorithm is used to enhance searchability. • Different scheduling results are given for different scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a new method of hierarchical fuzzy system modeling for high-dimensional regression problems is proposed, which is called multi-objective evolutionary hierarchical fuzzy regression system (MOEHFRS). Different from the existing hierarchical fuzzy systems with fixed topology, the proposed MOEHFRS improves the accuracy of the model, reduces the total number of rules, and eliminates unnecessary features by flexibly constructing topology. In the process of topology evolution, MOEHFRS can exchange and combine different sub-fuzzy systems to achieve the selection and reuse of important features, as well as the elimination of unnecessary features, which improves the diversity of topology and enables the model to be well applied to high-dimensional regression problems. Different combinations of sub-fuzzy systems will result in different performance and number of rules. A new multi-objective evolutionary optimization algorithm is proposed to simultaneously optimize the number of rules and the accuracy of the model, which achieves the balance between complexity and accuracy of MOEHFRS. The proposed method is validated on 13 real world regression datasets and compared with other 5 methods. The results show that MOEHFRS is effective and advanced in terms of accuracy, number of rules, retention of important features and universality in different regression problems. • Develop a multi-objective evolutionary hierarchical fuzzy regression system. • Propose a encoding method and evolutionary strategy for hierarchical fuzzy systems. • The system has good performance in complexity and precision. • Our approach is compared with other 5 methods on 13 regression datasets. [ABSTRACT FROM AUTHOR]

This paper investigates the secure defense control problem for memristive recurrent neural networks (MRNNs) under denial-of-service (DoS) attacks. Based on quantizer and logical processor techniques, a new secure defense control strategy is designed to maintain the performance of MRNNs subject to DoS attacks. The secure defense control strategy can not only effectively capture information about the dwell time of each DoS attack, but also can effectively reduce the amount of data to be transmitted and save communication resources. By constructing a Lyapunov–Krasovskii functional that depends on sampled information at t k and t k + 1 , some new stability criteria are derived for MRNNs under DoS attacks. By solving a set of LMIs, the control gain of the quantized sampled-data controller can be obtained. Thus, the corresponding secure quantized sampled-data controller is designed for MRNNs under DoS attacks. When there is no quantizer, the secure defense control problem for MRNNs under DoS attacks is also studied. Two numerical examples are given to confirm the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

This paper develops an effective distributed cooperative control approach with an event‐triggered communication mechanism (ETCM) for the active DC distribution network (DDN) system. Its control objective is to address the issues of voltage synchronization and power sharing among distributed renewable energy (DRE) while minimizing the utilization of communication resources. Firstly, a distribution network system model based on DC‐DC converters (DDCs) is established, and each DRE is regarded as a heterogeneous multi‐agent system (HMAS). Then, the distributed cooperative control problem of DREs is transformed into a cooperative output regulation (COR) problem with HMASs, which serves as an important foundation for controller strategy design. Secondly, considering different load demands in DDN system and the voltage of DC bus may deviate from its nominal value. Meanwhile, with the purpose of the proportional power sharing among DREs, this work proposes a distributed cooperative control approach under undirected communication topology, and designs a distributed observer based on dynamic ETCM while excluding Zeno behavior. In addition, the proposed control method can converge with a relatively fast speed, which improves the dynamic performance of the DDN system. Compared with the continuous time feedback control in DDN system, the proposed approach only needs the information of DRE itself and its neighboring DREs to accomplish the control objective, which further decreases the utilization of communication resources and reduces the update frequency of the controllers and switching loss of the DDCs. Finally, different simulation and experimental results are carried out to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This study focuses on the robust H∞$$ {H}_{\infty } $$ continuous‐discrete hybrid impulsive dynamic output feedback (DOF) control issue for time delays descriptor Markovian jump systems (TDDMJSs) with multidimensional impulsive operator. By applying an improved impulse‐time‐dependent Lyapunov–Krasovskii (L‐K) functional, the novel H∞$$ {H}_{\infty } $$ stochastic admissibilization conditions are formulated as linear matrix inequalities. A desired H∞$$ {H}_{\infty } $$ hybrid impulsive DOF controller is achieved, which includes a continuous DOF controller and a discrete impulsive DOF controller. The H∞$$ {H}_{\infty } $$ stochastic admissibilization of the closed‐loop TDDMJSs can be satisfied by virtue of the desired hybrid impulsive DOF controller. In the end, the validity of the proposed H∞$$ {H}_{\infty } $$ hybrid impulsive DOF controller design method is demonstrated by two examples, including an oil catalytic cracking process. [ABSTRACT FROM AUTHOR]

This article is concerned with the quantized control problem for neural networks with adaptive event‐triggered scheme (AETS) and complex cyber‐attacks. By fully considering the characteristics of cyber‐attacks, a mathematical model of complex cyber‐attacks, which consists of replay attacks, deception attacks, and denial‐of‐service (DoS) attacks, is firstly built for neural networks. For the sake of relieving the pressure under limited communication resources, an AETS and a quantization mechanism are employed in this article. By utilizing Lyapunov stability theory, adequate conditions ensuring the stability of neural networks are obtained. Moreover, the controller gain is derived by solving a set of linear matrix inequalities. At last, the usefulness of the proposed method is verified by a numerical example. [ABSTRACT FROM AUTHOR]

In this study, the security quantized control problem is investigated for discrete‐time Takagi‐Sugeno (T‐S) fuzzy systems with deception attacks based on the multi‐channel‐enabled round‐robin (MCERR) protocol. The MCERR protocol breaks through the limitation in the well‐known RR protocol where only one signal can be transmitted at every turn. Under MCERR protocol, multiple transmission channels are put to use and a set of sensor nodes can gain access to network. Considering the random deception attacks and the measurement outliers, a new system model is established and a sufficient condition is derived to ensure the stability of the discussed system. Furthermore, an outlier resistant observer‐based controller which can mitigate the impact of deception attacks is developed. Besides, the concrete expressions of observer gains and controller gains are obtained. Finally, the effectiveness of the proposed method can be validated by simulation results. [ABSTRACT FROM AUTHOR]

This paper is dedicated to dealing with the sampled measurement-based robust impulsive filter design problem for delayed Lur'e implicit hybrid systems via free-weighting matrix approach. Stochastic admissibility and H ∞ performance index for the implicit impulsive filtering error system are ensured based on a holistic impulse-time-dependent Lyapunov–Krasovskii functional, which embodies the impulse instants, time-varying delays and Markovian jump modes. The effect of sampled measurement output on the impulsive filter is considered, where the sampling moments are corresponding to the impulse instants. By applying the improved free-weighting matrix technique, the derivative of time-varying delay in implicit system states is no longer subject to the least upper bound 1. A set of linear matrix inequalities are applied to construe designing criterion for the desired mode-dependent implicit impulsive filter. The practicality of the developed approach is verified by nonlinear RC circuit. [ABSTRACT FROM AUTHOR]

• The aperiodic sampled-data system expression composed of some new variables is presented. • A novel loop-based Lyapunov functional is constructed and a stability criterion is presented. • The problem of RSE based on fragmentation method is estimated for aperiodic sampled-data system. In this paper, the sampled-data intervals comparison under stability criteria and reachable set estimation of aperiodic sampled-data systems are studied by means of uniform fragmented loop-based Lyapunov functional method. Firstly, by dividing the corresponding sampled-data intervals into sub-intervals on average, using fragmentation of system state and symbolic fragmentation expression, uniform fragmented loop-based Lyapunov functional is constructed. Furthermore, for the proposed stability criterion, a criterion for obtaining a larger upper bound of sampled-data is given in combination with the free weight matrix. Secondly, the reachable set estimation problem for aperiodic sampled-data systems with unit-peak bounded disturbance is investigated. Finally, the effectiveness of the proposed method is demonstrated by using numerical simulation and the experiment of vehicle active suspension device, it is also fully demonstrated that the proposed method can obtain the larger upper bound on the sampled-data interval and the more accurate reachable set estimation results. [ABSTRACT FROM AUTHOR]

This article considers the event-based security control problem for an interconnected system with Markovian switching topologies, where the Markovian process governs the switching of the communication topologies. Considering the effect of transmission constraints on control design, a Bernoulli-distributed variable is utilized to formulate the random network attacks. A sampling-data-based event-triggered communication mechanism is presented to decrease unnecessary transmission, where the average data-releasing rate can be decreased greatly. Considering the effects of deception attacks and event-triggered mechanism simultaneously, an observer-based switched control system model is established. By combining this new model and employing the proper Lyapunov–Krasovskii functional, some sufficient criterions are derived such that the closed-loop error system is exponentially mean-square stable with the desired disturbance attenuation, and the controller and observer gain matrices will be tackled simultaneously via the solution of linear matrix inequalities. Finally, this article employs a simulation example to verify the availability of proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, the stochastic sampled-data exponential synchronization problem for Markovian jump neural networks (MJNNs) with time-varying delays and the reachable set estimation (RSE) problem for MJNNs subjected to external disturbances are investigated. Firstly, assuming that two sampled-data periods satisfy Bernoulli distribution, and introducing two stochastic variables to represent the unknown input delay and the sampled-data period respectively, the mode-dependent two-sided loop-based Lyapunov functional (TSLBLF) is constructed, and the conditions for the mean square exponential stability of the error system are derived. Furthermore, a mode-dependent stochastic sampled-data controller is designed. Secondly, by analyzing the unit-energy bounded disturbance of MJNNs, a sufficient condition is proved that all states of MJNNs are confined to an ellipsoid under zero initial condition. In order to make the target ellipsoid contain the reachable set of the system, a stochastic sampled-data controller with RSE is designed. Eventually, two numerical examples and an analog resistor–capacitor network circuit are provided to show that the textual approach can obtain a larger sampled-data period than the existing approach. [ABSTRACT FROM AUTHOR]

• Few people consider the data-driven parameter adaptive control in the tracking control of AUV. Based on the traditional sliding mode control, a novel adaptive tracking control considering the external environment interference and uncertainty is designed using a data-based online ADHDP method. • The Lyapunov function of ADHDP algorithm considering experience replay is designed to obtain the stability results and ensure the convergence of data-driven neural network weights. • The weighted experience replay method is considered, and the historical data is weighted with a novel parameter λ and is used for the control strategy online real-time update. At present, there are few simultaneous studies. A novel adaptive trajectory tracking control method is proposed in this paper for autonomous underwater vehicle systems with external disturbances. The control action is composed of sliding mode control and action-depended heuristic dynamic programming (ADHDP) controller to track the expected sailing position and angle in the horizontal coordinate system with fixed depth. As an auxiliary control of sliding mode control, the ADHDP controller observes the difference between the actual sailing position/angle and the expected sailing position/angle, and adaptively provides corresponding supplementary control actions using a data-driven fashion. At the same time, we design a weight-related priority experience replay (PER) technology to update the online weight network by using the relevant historical data stored in the database to improve the learning rate. The proposed algorithm can adjust parameters online under various conditions. Furthermore, it is very suitable for underwater vehicle systems with parameter uncertainties and external disturbances. Based on Lyapunov stability method, the stability of closed-loop system state and network weight error is analyzed. Finally, the validity of our control strategy is verified by simulation. [ABSTRACT FROM AUTHOR]

In this article, we investigate the attack‐parameter‐dependent controller synthesis problem for a class of stochastic control system under aperiodic denial‐of‐service (DoS) attacks. Different from the classic DoS attack models, which are characterized by DoS frequency and DoS duration, a new type of time‐constrained DoS attack model that only restricts the upper and lower bounds of the sleeping and active periods of the DoS attacker is introduced. Considering the effect of such DoS attacks, a switching‐like stochastic system model is built. Then based on the attack‐parameter‐dependent time‐varying stochastic Lyapunov function method, a new mean square exponential stability criterion of the resulting stochastic system under the DoS attacks is formulated in the form of linear matrix inequalities. Also, the attack‐resilient H∞$$ {H}_{\infty } $$ controller is designed. Finally, taking an aircraft system as an example, the validity of the obtained theoretical results is verified. [ABSTRACT FROM AUTHOR]

This article immerses in the event‐triggered containment control problem for a Lipschitz‐like nonlinear multi‐agent system with multiple active leaders. First, two event‐triggered mechanisms with internal dynamic variables are designed in both sensor‐observer (S‐O) and controller‐actuator (C‐A) channels to monitor data transmission and reduce communication burden. Second, an observer is constructed with the aid of the output information to estimate the states that cannot be acquired directly. Then, a control protocol considering time‐varying communications delays is proposed based on the observer. Third, the boundedness analysis of the containment error system is constructed by Lyapunov stability theory, the sufficient conditions are given in light of strict feasible LMIs. Finally, the validity of the proposed controller is verified by two simulations. [ABSTRACT FROM AUTHOR]