This study aims to present the sampled-data-based state estimation for delayed chaotic neural networks (DCNNs) subject to stochastic deception and false data injection attacks, together with random packet losses that occur during signal transmissions. In this work, the dual phase fragmentation approach is implemented that utilizes variable sampled outputs to ensure accurate signal updates with fractional parameter α, effectively capturing sampling dynamics of chaotic networks and maximizing system information utilization. A looped-type Lyapunov functional (LTLF) is constructed over the segmented switching phases to establish sufficient conditions guaranteeing the existence of the state-estimator for the desired DCNNs, even in the presence of attacks and packet dropouts. The proposed conditions are derived through the incorporation of free-weighting matrix integral inequalities and Wirtinger-based inequality in terms of linear matrix inequalities (LMIs), thereby achieving reduced conservatism and improved analytical tractability by invoking Lyapunov stability theory. To verify the validity and efficiency of the proposed theoretical results, two illustrative numerical examples are conducted. Among them, one example involves a real-time implementation based on the quadruple tank process system (QTPS), demonstrating the practical relevance by performing integral absolute error (IAE), integral squared error (ISE), and estimation energy (EE) and robustness of the sampled-data state estimation approach for DCNNs.

This article focuses on addressing the finite-time dynamic output feedback control problem for periodic piecewise nonlinear systems in the midst of multifaceted disturbances and cyber attack scenarios. Moreover, multifaceted disturbances encompass mismatched disturbances and multiple matched disturbances. Therein, the mismatched disturbances are presumed to be norm-bounded vectors, whereas multiple matched disturbances emanate from exogenous systems. Additionally, two instances of nonlinear dynamics in the plants are looked at, pertaining to both known and unknown cases. Furthermore, to estimate the multiple matched disturbances, output-reliant multiple periodic piecewise disturbance observers for both the nonlinear instances are framed separately on the grounds of the control input and the measurement output. Moreover, the mismatched disturbances are attenuated using the mixed H infinity/passivity approach. From thereon, a holistic control framework blending these observers with a dynamic output feedback mechanism is put together to guarantee the intended closed-loop performance in a finite-time span by rejecting the matched disturbances. Therein, a probabilistic model with Bernoulli-distributed variable is laid out to characterize the incidence of deception attacks, potentially improving the resilience of the control. Following that, by means of Lyapunov stability theory, the stability of the target system is analyzed and the precise setups of the periodic piecewise controller and disturbance observer gains are put forward. Ultimately, simulation results are put forward to confirm the reliability and effectiveness of the control protocol.

This paper investigates the problems of fixed-time (FXT) synchronization for stochastic fuzzy neural networks subject to hybrid impulses by introducing improved FXT stability criteria and developing two simple control strategies. First, a novel FXT stability criterion is presented, which effectively extends the existing classical FXT stability theorems for general nonlinear dynamical systems to impulsive stochastic nonlinear systems. The proposed settling time (ST) estimation offers greater accuracy and explicitly establishes a relationship among impulse interval, impulsive strength, and system intrinsic parameters. Subsequently, new FXT synchronization conditions are rigorously derived for the addressed system, accounting for hybrid impulsive effects, stochastic disturbances and fuzzy logics via designing suitable control schemes. Unlike conventional controllers, the proposed approach utilizes hyperbolic sine function, which not only efficiently reduces the hassle of frequently regulating control parameters, but also significantly mitigates the chattering phenomenon caused by the discontinuous sign function. Finally, the validity of the theoretical results is further evaluated via numerical simulations and an application to DNA encoding based image encryption.

This article focuses on addressing the cluster formation of multiagent systems (MASs) under cyber attacks. The main motive of this article is to formulate a dynamic adaptive event-triggered (DAET) controller to efficiently reduce the network burden. To ensure the required cluster formation, the communication topology describing the information flow among agents is partitioned into various clusters. The agents within the same cluster need to perform certain formation tasks, while agents in different clusters need to carry out various formation tasks. Furthermore, to ensure cybersecurity, the controller is designed subject to deception attacks. Asuitable Lyapunov-Krasovskii functional is designed to establish sufficient conditions for achieving the required cluster formation of the considered MAS. At last, two numerical examples are presented to verify the capability of the derived theoretical results.

This research aims to address the problem of obtaining resilient secure disturbance rejection control for parabolic partial differential equation systems governed by a semi-Markovian jump process in the presence of parameter uncertainties, multiple cyber attacks, multiple disturbances, additive and multiplicative gain perturbations. In particular, by employing a mode-dependent interconnected disturbance estimator framework, the matched disturbances originating from exogenous systems can be effectively addressed with high estimation accuracy. After that, a resilient secure interconnected disturbance rejection control is designed to accomplish the desired stabilization and disturbance rejection objectives. Therein, multiple cyber attacks encompassing both deception and denial-of-service attacks, along with additive and multiplicative gain perturbations, are incorporated into the controller structure to enhance the system’s protection and resilience. Following that, the necessary criteria for achieving stochastic stabilization of the system under consideration are acquired within the context of linear matrix inequalities through the application of Lyapunov stability theory along with the integral-based Wirtinger’s inequality. In conclusion, two numerical simulations are conducted to confirm the efficiency and applicability of the designed control technique.

In this paper, a class of discontinuous Cohen-Grossberg neural networks with time-varying delays is considered. Firstly, under the extended Filippov differential inclusions framework, the problem of periodic solutions of the considered neural networks with more relaxed conditions imposed on the amplification functions is analyzed by using set-valued mapping and Kakutani’s fixed point theorem, which has rarely been used to study such problem. Secondly, the fixed-time synchronization of the error system of the considered neural networks is also investigated by designing a novel control strategy, which can improve not only the previous ones with sign function greatly, but also can reduce the chattering phenomenon. Finally, two numerical examples are presented to further illustrate the validity of the obtained results.

Structural systems are subject to uncertainties related to material properties, loading conditions, and environmental factors, which can make it difficult to design accurate control strategies. Conventional control systems strongly depends on the structural dynamics. However, under various perturbations with unknown frequencies, the predefined structure equations get unreliable, and also un-modeled dynamics are triggered. In this study, an active mass damper (AMD) is designed based on a new type-3 fuzzy controller (T3FC). The structural dynamics in the suggested approach are identified using an interval type-2 fuzzy restricted Boltzmann machine (IT2F-RBM). Then, the online identified model is applied to optimize T3FC. The rules of T3FC are tuned using Square Root Cubature Kalman Filter (SCKF). The designed control scheme does not rely on the structure dynamics, and it has a strong capability to deal with uncertainties and un-modeled dynamics. All control and model parameters are tuned online to cope with structural perturbations. The proposed controller has a good learning ability and can provide faster and more practical control response. So, the main contribution to artificial intelligence (AI) is designing a new T3FC based on an online learned IT2F-RBM-based model, and the contribution to the application of AI is implementing it on a real structural control system. Several simulations and experimental examinations are given to demonstrate the ability of the suggested controller. The results from simulations and experiments indicate that the introduced scheme outperforms the basic controllers, and the ratio of vibration suppression is more than 80%.

This paper aims to derive more generalized Lyapunov inequality conditions and establish new fixed-time stability lemmas for Filippov systems. Using the definition of fixed-time stability and advanced inequality techniques, we rigorously prove that the zero solution is fixed-time stable. Furthermore, we provide detailed theoretical analysis showing that setting r=1 in the generalized economical inequality conditions yields the minimal settling time. These results not only improve upon existing fixed-time stability lemmas, but also validate previous related hypotheses. As a key application, the newly established fixed-time stability lemmas are employed to study synchronization and anti-synchronization in fixed-time for master–slave discontinuous leakage-delayed competitive neural networks modeled by Filippov systems. Leveraging differential inclusion theory and delay-free non-chattering controllers, we derive leakage-delay-dependent criteria to ensure synchronization, marking the first theoretical results in this domain. In addition, the derived settling times are also leakage-delay-dependent, revealing the explicit influence of leakage delays on convergence speed. Finally, numerical simulations are presented to verify the correctness of the main theoretical findings.

This article centers on addressing the fault-tolerant tracking control protocol problem for singularly perturbed hyperbolic partial differential equation systems in the midst of probabilistic time delays, multimodal injection attacks, actuator faults, and external disturbances. Succinctly, an improved extended state observer is tailored to yield concurrent and accurate estimation of plant states and external disturbances. Therein, the designated observer is endowed with gain perturbations and multimodal injection attacks to strengthen the resilience of the observer. In the sequel, with the information obtained from the estimator, an improved extended state observer-based fault-tolerant tracking control law is set forth, which greatly assists the analyzed model to attain the steady tracking ability by eliminating the traces of disturbances. Moreover, the actuator faults are considered in the controller channel to enhance the reliability of the tracking performance. Furthermore, through the construction of the Lyapunov-Krasovskii functional, the criteria for ascertaining the tracking objective of the configured system are articulated by means of a linear matrix inequality framework. Subsequently, the desired controller and observer gains are obtained with the aid of established criteria. Ultimately, the significance of the analyzed findings is assured through numerical simulations.

This investigation deals with the accomplishment of inter-layer synchronization in multi-layer complex networks that are influenced by both matched and mismatched disturbances. The matched disturbances refer to those arising from the external system and are counteracted with the design of output disturbance observer, whilst the mismatched ones are alleviated by means of an extended passivity performance technique. Nonetheless, it is essential to monitor the usage of communication resources along with the associated burdens due to the growing need for the reduction of limited available resources in networks. Thereby, this study fundamentally emphasizes the infusion of a hybrid-triggered control approach for multi-layer networks alongside the disturbance rejection technique referred to earlier. In precise, this hybrid-triggered control a combination of both time and event-triggered control schemes brewing the perks of both the schemes together. And the sufficient conditions confirming the synchronization of the intended multi-layer complex networks are obtained in terms of linear matrix inequalities, for which Lyapunov stability theory accompanied by integral inequality and convex optimization technique are implemented. In conclusion, the practicality and correctness of the adopted strategy are presented in the numerical section with simulation outcomes.

This paper presents a solution to the limitations of traditional vibration control methods, which often depend on precise structural parameters and mathematical models, leading to poor performance under real-world uncertainties and nonlinearities. The study introduces an vibration control system based on adaptive Active Rotary Inertia Driver (ARID) systems. This system integrates three key components: fractional-order dynamic fuzzy modeling for online system identification, a self-structuring Type-3 Fuzzy Logic System (T3-FLS) with non-singleton fuzzification to handle sensor noise and uncertainties, and an adaptive compensator based on the H infinity theorem to ensure robustness against disturbances and parameter variations. The T3-FLS employs a new self-structuring algorithm that autonomously optimizes rule databases, membership functions, and parameters in response to dynamic conditions, addressing a gap in the existing literature regarding self-structuring mechanisms for T3-FLSs in vibration control applications. Experimental/simulation validation demonstrates the superiority of the proposed system compared to conventional methods. In experiments/simulations, the proposed algorithm achieved a peak angle of 0.009/0.005 rad and an RMS of 91.5%/95.7%, showing significant improvements over conventional methods, which only achieved 0.7%/1.4% and 0.8% /2.3% under perturbed dynamics (see the video of implementation at https://youtu.be/OWS8Ums95sQ.

In this work, the filtering design issue for nonlinear networked periodic piecewise systems in the midst of external disturbances and probabilistic time-varying delays is investigated. Specifically, the interval type-2 fuzzy approach is utilized to model the nonlinear periodic piecewise plant. Moreover, prime attention is focused on designing the interval type-2 fuzzy-based non-fragile filter in the periodic piecewise framework, utilizing the measurement output of the considered system model. The filter design accounts for stochastic deception attacks on the measurement output and incorporates gain perturbations to improve robustness and resilience. Subsequently, through the formulation of a periodic piecewise Lyapunov-Krasovskii functional, sufficient criteria are derived in the framework of linear matrix inequalities to ensure the stability of the filtering error system. Based on the established stability criteria, the design conditions of periodic piecewise filter gain parameters are determined. Eventually, simulation outcomes are offered to showcase the efficiency of the developed theoretical outcomes.

This research looks at the two-sided looped functional-oriented synchronization issue for semi-Markov jump neural networks under the context of various susceptible facets, namely, time-varying delays, hybrid cyber attacks and external disturbances through the secured event-trigger control mechanism. To start off, an event-trigger control technique is developed for lowering the communication overhead without cutting back on control performance. The formed control regulation not only covers synchronization but also integrates security considerations by factoring in hybrid cyber attacks, including deception and denial-of-service attacks. And, the stochastic nature of those attacks is modelled using the Bernoulli distribution. Besides that, a two-sided looped type Lyapunov function is designed, which takes full advantage of the sampling interval from y(te) to y(te+1), leading to less conservative stability requirements and a longer allowable sampling period. A notable benefit of the postulated method is that it gets rid of the conventional necessity for a typical positive definite matrix in Lyapunov configuration. Based on the constructed Lyapunov functional, relevant constraints for guaranteeing the synchronization are set out in the frame of linear matrix inequalities and from there, the mode-dependent control gains are reckoned. Also, the traces of disturbances are handled by leveraging the (M,N, O)-v dissipative performance. In the closing of this research, to support the efficacy and usefulness of the laid-out control law and the outlined theoretical findings, two example studies, one of which is the quadruple-tank process model, complemented by the graphical illustrations, are offered.

In this work, we develop a disturbance suppression-oriented fuzzy sliding mode secured sampled-data controller for third-order parabolic partial differential equations that ought to cope with nonlinearities, hybrid cyber attacks, and modeled disturbances. This endeavor is mainly driven by formulating an observer model with a T–S fuzzy mode of execution that retrieves the latent state variables of the perceived system. Progressing onward, the disturbance observers are formulated to estimate the modeled disturbances emerging from the exogenous systems. In due course, the information received from the system and disturbance estimators, coupled with the sliding surface, is compiled to fabricate the developed controller. Furthermore, in the realm of security, hybrid cyber attacks are scrutinized through the use of stochastic variables that abide by the Bernoulli distributed white sequence, which combat their unpredictability. Proceeding further in this framework, a set of linear matrix inequality conditions is established that relies on the Lyapunov stability theory. Precisely, the refined looped Lyapunov–Krasovskii functional paradigm, which reflects in the sampling period that is intricately split into non-uniform intervals by leveraging a fractional-order parameter, is deployed. In line with this pursuit, a strictly (Φ1,Φ2,Φ3)−ϱ dissipative framework is crafted with the intent to curb norm-bounded disturbances. A simulation-backed numerical example is unveiled in the closing segment to underscore the potency and efficacy of the developed control design technique.

This article studies the dynamic event-triggered state estimation problem for nonlinear systems under weighted try-once-discard (WTOD) protocol and replay attacks. To improve resource utilisation, a dynamic event-triggered strategy is proposed. Meanwhile, a WTOD protocol is designed, which allows only one sensor unit to send measurement data at each transmission instant, thereby controlling the communication data between the sensor unit and the observer. This protocol can effectively avoid data conflicts and reduce communication burdens. Moreover, replay attacks launched by attackers replace the current innovative data with historical data by tampering with communication data. To address this issue, this paper derives sufficient conditions to ensure that the augmented system is mean-square exponentially stable. The observer gain parameters are determined by solving linear matrix inequalities. Finally, the effectiveness and feasibility of the simulation experiments are verified through simulation examples.

Distributed parameter cyber-physical systems are increasingly deployed in applications with spatially distributed dynamics, where cyber attacks and communication constraints pose major obstacles to reliable system operation. In this regard, this paper develops a grey wolf optimization-driven intelligent memory event-triggered control strategy for distributed parameter cyber-physical systems described by partial differential equations in the presence of multimodal false data injection attacks and external disturbances. Specifically, an observer is designed by blending the saturation function in the innovation term to estimate the system states, where the saturation function mitigates the influence of abnormal measurement variations caused by deceptive signals, thereby improving estimation reliability. Furthermore, in light of the estimated state information, a feedback control protocol is developed in conjunction with an intelligent memory event-triggered mechanism, which effectively reduces communication load by utilizing both current and previously transmitted data. Subsequently, the triggering threshold parameter is optimized utilizing the grey wolf optimization algorithm, which provides a desirable balance between communication efficiency and control performance. Moreover, by utilizing Lyapunov stability theory, the stability of the closed-loop system is analyzed, leading to tractable conditions expressed in terms of linear matrix inequalities. Ultimately, numerical simulation results are offered to corroborate the relevance of the outlined theoretical insights.

In this paper, a new control approach is proposed for the synchronization and stabilization of a class of chaotic systems with unknown dynamics and input nonlinearities. Type-3 fuzzy logic systems (T3-FLSs) are developed to adaptively model the dynamics of both master and slave systems in real time. The input is affected by sector-bounded hysteresis and quantization, and these challenges are explicitly addressed in the control design. Unlike conventional methods, the proposed strategy does not require prior knowledge of the system equations or the derivatives of system signals. The adaptation laws for the T3-FLS parameters and estimation errors are rigorously derived using stability and robustness analysis, ensuring smooth control signals without chattering. Extensive simulations and real-time examinations demonstrate that the method achieves accurate synchronization even under severe uncertainties, high levels of random noise, and non-identical chaotic systems. Comparative results confirm the superiority of the proposed approach over existing fuzzy control methods.

This study delves into exploring dissipative synchronization for a class of switched neural networks with external disturbances featuring reaction-diffusion terms under the master-slave scheme. Precisely, the addressed network model comprises a hybrid attack model which entails both deception and denial-of-service attacks. Moreover, security-based control is designed to achieve the intended results, wherein in the realm of control design, the likelihood of cyber attacks is dictated by two separate and independent stochastic Bernoulli distributed factors. Meanwhile, the dissipative theory is employed to effectively curb the external disturbances within the network model. Subsequently, by leveraging the Lyapunov stability theory and linear matrix inequality approach, adequate conditions are acquired for ensuring the mean square exponential synchronization and strict (Gamma(1), Gamma(2), Gamma(3))-theta dissipativity of the examined system. Furthermore, the relation for deriving the control gain matrices is set forth in accordance with the acquired criteria. At the end, a numerical example accompanied by simulation results is supplied to vividly demonstrate the efficacy and significance of the acquired theoretical insights.

This work describes the dissipative constraint-based load frequency control problem for multi-area power system under load disturbances. Particularly, a new model incorporating time-varying delays and cyberattacks are widespread in communication networks, significantly impacting control and stability. Consequently, the state-space equations of the addressed model are formulated and analyzed under the impact of false data injection attacks, and time-varying delays. The analysis is simplified by representing cyber-attacks using nonlinear functions adhering to Lipschitz continuity, while possible cyber-attacks are characterized by stochastic parameters conforming to Bernoulli distributions. Followed by the above information, stochastic analysis and Lyapunov-Krasovskii stability theory, the convex optimization problem is formulated. As a consequence, the load frequency control gains were effectively constructed, confirming that the established power model is stochastically stable and strictly (Q, S, R) -y-dissipative. Finally, the case studies are employed to examine the usefulness of the suggested scheme.

The focus of this study is on investigating the design of dynamic hybrid-triggered resilient control for partial differential equation (PDE) systems even in the presence of Neumann boundary conditions. To be specific, the PDE under consideration is of the parabolic type involving cyber-physical switched systems and is subject to randomly occurring uncertainties, external disturbances and false data injection (FDI) attacks. Moreover, in an attempt to minimize the volume of data transfers, a broader dynamic hybrid-triggered (DHT) approach is executed, amalgamating both time-triggered and dynamic event-triggered techniques. Concurrently, resilient control is being considered to guarantee the required stabilization of the system, even in the presence of gain fluctuations. Within the specified context, stochastic variables that conform to the Bernoulli distribution are incorporated in the DHT resilient scheme and FDI attacks. Furthermore, the construction of a pertinent Lyapunov-Krasovskii functional leads to the establishment of required conditions for ensuring both asymptotic stability and extended dissipative performance for the closed-loop structure. Moreover, the required controller gain matrices are derived through the utilization of linear matrix inequalities. Ultimately, the suggested control design technique's effectiveness is showcased through the presentation of two numerical examples.