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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]

Summary: The problem of robust fault estimation (FE) for a class of nonlinear systems in discrete‐time domain is studied via proposing a comprehensive partition‐based switching scheme. Both enhancement factors and attenuation factors are designed synchronously for accomplishing a comprehensive partition of the entire space spanned by the considered normalized fuzzy weighting functions. Thanks to the proposed comprehensive partition‐based switching scheme, six subspaces would be obtained by identifying the real‐time permutation order of the underlying parameters. Therefore, a novel fuzzy FE observer with six kinds of switching modes can be on‐line implemented. As a result, the robust H∞ performance index can be enhanced since much more information is employed than those reported results. Finally, simulation comparisons over the existing results are provided to show the obtained advantages of the article. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reducing the conservatism for stabilization of discrete-time Takagi–Sugeno fuzzy systems via a new extended representation approach. Different from previous slack variable methods, an advanced slack variable method without limitations is proposed for further making good use of available fuzzy weighting functions. Owing to the introduction of extended representations for homogenous polynomials, those strong limitations in recent literature are relaxed and thus the primary and secondary sequences of all normalized fuzzy weighting functions can be mapped to one augmented multi-indexed matrix for each homogeneous polynomial. Therefore, less conservative stabilization conditions can be provided as a result of these effective measures and thus a part of the online computational cost is transferred to the offline implementation. Finally, the benefits of the proposed method are illustrated by means of numerical experiments. [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]

Abstract This paper investigates the optimal tracking control problem (OTCP) for continuous-time non-linear systems with input constraints. A novel event-triggered single-network adaptive dynamic programming (ADP) method is proposed to obtain the solution of constrained OTCP. By constructing an augmented system and introducing a novel discounted non-quadratic cost function, an event-triggered constrained tracking Hamilton–Jacobi–Bellman equation is formulated. Then, only a critic neural network (NN) is employed to learn the optimal value function and further obtain the optimal tracking controller, which enables the architecture of ADP implementation to be simpler. And a novel NN weights updating law is constructed, by which the restriction of initial admissible control is removed. Based on the Lyapunov theory, the convergence of critic NN weights and the stability of closed-loop system are demonstrated. The derived optimal tracking controller is updated only at the event-triggered instants decided by the designed event-triggered condition. Therefore, the communication burden can be reduced effectively. Finally, two simulation examples are given to verify the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper is focused on the issue of scheduling stabilization of Takagi–Sugeno fuzzy control systems by the aid of digging down much deeper of implicit information in the underlying systems. An event-triggered real-time scheduler that decides which control mode should be executed at any given instant is constructed by periodically evaluating the joint-distribution-type of multi-instant normalized fuzzy weighting functions at every sampled instant. Profiting from the proposed event-triggered scheduling policy, the proper control mode for the current instant is updated in order to adapt time-varying situations once if the underlying joint-distribution-type changes, and thus previous implementations of control tasks with an unchanged control mode can be further relaxed in this paper. The effectiveness of our approach is verified by several simulation examples in the end. [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]

The observer-based state estimation of discrete-time nonlinear systems is carried forward by designing a new maximum-priority-based fuzzy observer. The significant distribution information about the current-time normalized fuzzy weighting functions is well employed by means of a maximum-priority-based switching law. Compared with the existing results, a new fuzzy observer can be provided for the sake of giving less conservative result over previous results proposed in the literature. Finally, several illustrative examples are provided for showing the advantage of the result given in this paper. [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]

Robust fault estimation (FE) observer designs for discrete-time nonlinear system are deeply discussed via a joint real-time scheduling law. Different from previous results involved in the field, a fresh FE observer is produced by means of intermittently launching a joint real-time scheduling law in the light of updated multi-instant information. Thanks to the overall vision of system information across several adjacent sampled points, less conservative result is favorably acquired. Second, the given result is ulteriorly ameliorated by effectively developing an improved introduction technique of free matrix variables. Finally, one nonlinear truck-trailer example is employed for validating the superiority of theoretic fruits that is given in this paper. [ABSTRACT FROM AUTHOR]

The paper focuses on less conservative design of observer-based output feedback control synthesis of discrete-time Takagi–Sugeno fuzzy systems on account of an improved homogenous polynomial approach. Compared with those previous methods in the cited literature, a novel fuzzy observer-based output feedback law is constructed in virtue of utilizing one promising solution to homogenous polynomials that are parameter-dependent on both the current-time and those multi-steps past-time normalized fuzzy weighting functions with a pair of prescribed degrees. It is rather remarkable that more information contained in the normalized fuzzy weighting functions is integrated to build the proposed control law, and thus the conservatism left behind previous results can be released in this work. Moreover, a robust version of the underlying result is also proposed. Finally, the effectiveness of the given approach is demonstrated via some illustrative experiments. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of model reduction for discrete-time Takagi–Sugeno (T–S) fuzzy state-delay systems with finite-frequency input signals. A new finite-frequency model reduction algorithm is proposed, which can get a better approximation performance than the existing full-frequency methods. Firstly, a finite-frequency H ∞ performance index is defined in the frequency domain. Then, a stability condition and a finite-frequency H ∞ performance analysis condition are developed by the aid of Jensen׳s inequality and Parseval׳s theorem, respectively. Based on these conditions, sufficient model reduction conditions are derived for discrete-time T–S fuzzy state-delay systems. An optimization algorithm is proposed to obtain a stable reduced-order model satisfying the finite-frequency performance specification. Finally, the effectiveness of the proposed method is illustrated by a numerical example. [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]

In this study, the so-called multi-instant fuzzy control design is discussed for a class of nonlinear networked systems with unreliable communication channels. Different from most existing results in the references, we aim to design a new multi-instant fuzzy controller such that the variation information between two fore-and-aft normalized fuzzy weighting functions is considered in the process of control design. Based on the above effort, a class of slack variable technique that is fit for the multi-instant case with higher efficiency as well is applied by extending some key matrix transform results. Finally, some simulation results are employed to illustrate the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This study addresses the problem of releasing the conservatism of stability criteria for continuous-time T–S fuzzy systems on the basis of an extended approach. Recently, previous stability conditions have been relaxed by exploring the positive and negative properties of the time derivatives of normalized fuzzy weighting functions but the underlying approach is not very mature. By modeling the membership space of the time derivatives of normalized fuzzy weighting functions via selected binary numbers and decimal numbers, their positive and negative properties are more fully considered and some redundant terms are transformed as a positive role instead of being simply removed. Thus, less conservative stability criteria can be achieved. Finally, the effectiveness of the proposed approach is illustrated by numerical experiments. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of fuzzy H ∞ control design for a class of discrete-time nonlinear control systems via a multi-samples approach. A new fuzzy H ∞ controller, which is parameter-dependent on not only the current-time but also the one-step-past membership functions, is developed to derive less conservative design conditions for ensuring both the asymptotic stability and the prescribed H ∞ performance index of the obtained closed-loop system. Thanks to the usage of both a new fuzzy Lyapunov function and an extension of the homogenous matrix polynomial methodology, linear matrix inequality (LMI)-based H ∞ control design conditions are obtained for minimizing the normal level in a systematic and relaxed way. In addition, further efforts are made by developing an efficient slack variable approach. Finally, two numerical examples are provided to illustrate the effectiveness of the results given in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the problem of observer-based output feedback control design of discrete-time Takagi–Sugeno (T–S) fuzzy systems is investigated based on a multi-samples method. In order to ameliorate the existing results in the literature, a fuzzy observer-based output feedback controller, which relies on multi-samples normalized fuzzy weighting functions, is developed for relaxing the design conditions of observer-based output feedback control. Moreover, thanks to the special form of the developed observer-based output feedback controller, it is shown that all the designed parameters can be solved via a set of strict linear matrix inequalities, i.e., the drawback induced by the usual two-step method is resolved by means of a compromising way. Finally, the benefit of the proposed result is validated by using some numerical experiments. [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]

This paper proposes a novel H\infty filtering technique for a class of discrete-time fuzzy systems. First, a novel kind of fuzzy H\infty filter, which is homogenous polynomially parameter dependent on membership functions with an arbitrary degree, is developed to guarantee the asymptotic stability and a prescribed H\infty performance of the filtering error system. Second, relaxed conditions for H\infty performance analysis are proposed by using a new fuzzy Lyapunov function and the Finsler lemma with homogenous polynomial matrix Lagrange multipliers. Then, based on a new kind of slack variable technique, relaxed linear matrix inequality-based H\infty filtering conditions are proposed. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approach. [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]