This article proposes a resilient control framework for securing cyber-physical systems (CPSs), specifically addressing nonlinear descriptor systems operating under communication constraints and subject to sensor and actuator attacks. We integrate Takagi-Sugeno (T-S) fuzzy models with a Q-learning-based event-triggered mechanism (ETM) and adopt a sliding-mode control strategy to establish a resilient security architecture that adaptively balances operational efficiency with robust protection against cyber-physical threats. A major contribution of this work lies in designing an adaptive fuzzy sliding-mode observer (SMO) with mismatched premise variables for the estimation of compromised system states. Additionally, a sliding-mode controller (SMC) is synthesized to maintain closed-loop admissibility and ensure the reachability of sliding surfaces. We advance beyond the existing approaches by employing the secretary bird optimization algorithm (SBOA) to optimize controller and observer gains, thereby solving the nonconvex optimization challenges present in controller and observer design. The effectiveness of the proposed method is validated through extensive Monte Carlo simulations on a truck-trailer system. These simulations demonstrate the efficacy of the approach in maintaining system stability and performance under various attack scenarios, thereby making a significant contribution to the security of nonlinear systems in networked environments.

Abstract The sampling data control of bidirectional associative memory (BAM) neural network with leakage delay is considered in this article. The BAM model is viewed as a mixed delay that combines a distributed delay, a discrete delay that varies over time, and a delay in the leaking period. The sampling system is then converted to a continuous time-delay system using an input delay method. In order to get adequate conditions in the form of linear matrix inequalities(LMIs), we build a new Lyapunov-Krasovskii Functional (LKF) in conjunction with the free weight matrix approach. Finally, a simulation results are given to show the efficiency of the theoretical approach.

Abstract The stabilization problem of stochastic delayed reaction-diffusion Cohen–Grossberg BAM neural networks (SDRDCGBAMNNs) with impulsive effects and boundary control is studied in this paper. By using suitable boundary controllers, Lyapunov–Krasovskii functional, linear matrix inequalities and average impulsive interval method, new sufficient criteria are found to ensure that the SDRDCGBAMNNs achieve boundary stabilization in finite-time. Based on these criteria, the effects of impulsive and boundary controllers on finite-time stability are analyzed. Numerical simulations are performed to demonstrate the feasibility of the theoretical results.

This paper addresses the global exponential stability in Lagrange sense for memristor-based neural networks (MNNs) with time-varying delays. This paper attempts to derive the delay-dependent Lagrange stability conditions in terms of linear matrix inequalities by designing a suitable Lyapunov-Krasovskii functionaland used Wirtinger inequality, Jensen-based inequality for estimating the integral inequalities. The conditions which are derived confirms the globally exponential stability in Lagrange sense for the proposed MNNs and, the detailed estimation for global exponential attractive set is also given. To show the effectiveness and applicability of the proposed criteria, two numerical examples are also provided in this paper.

In this article, the Mittag-Leffler synchronization (MLS) problem of a fractional-order permanent magnet synchronous generator (FOPMSG)-based wind turbine system against unknown disturbances, such as external load torque variations and system parameter uncertainties, an adaptive fractional sliding-mode control (AFSMC) method is proposed based on improved convergence rate performance of the FOPMSG to track accuracy, response speed, and robustness. The AFSMC method is based on a fractional-order term incorporated into the new law for reaching the sliding mode, improves the chattering in the control signal, and reduces the time required for the system to reach the sliding-mode surface. Sufficient conditions are derived to ensure the robust MLS for the sliding-mode dynamics by the designed robust controller. In this article, for the first time, an adaptive sliding-mode control (ASMC) with a terminal function that accurately controls the FOPMSG model at a prespecified time is proposed. Moreover, the designed ASMC can effectively attenuate the existence of disturbances and uncertainties by eliminating the reaching phase based on the Lyapunov stability theory. Finally, the simulation results applied to the FOPMSG model show that the proposed control method has better disturbance rejection ability, fast dynamic response, and suppression of the chattering effect.

This article studies dissipativity analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with time delays. Two specific activation functions are considered along with common bounded and activation functions of Lipschitz-kind. Since quaternion multiplication is not commutative, we must divide the model, which is evaluated by quaternion, into four elements that are real-valued elements. On the basis of the construction of novel Lyapunov functional, and applying fractional-calculus theory, new criteria for the test of the global dissipativity and exponential stability of FOQVNNs model are established. FOQVNNs have also been suggested to provide global dissipativity and exponential stability, whereas nonlinear complex activation functions are constrained by the usage of linear matrix inequality methods, which utilize quaternion matrices and positive quaternion definite matrices. Finally, the effectiveness and superiority of the proposed approach is validated through numerical examples.

In this paper, the leader-following cyber secure consensus problem for discrete-time fractional-order multi-agent systems (DFOMASs) in the present of denial-of-service attacks by means of distributed delayed control strategy is investigated. As MASs work in networked environments, their security control becomes critically desirable in response to various cyberattacks, such as denial of service (DoS). The resulting topologies caused by DoS attacks may destabilize the consensus performance of MASs. Especially under connectivity-broken attacks, the connectivity between agents is destroyed. To deal with these difficulties, a novel defense strategy consisting of distributed delayed consensus control is proposed. To guarantee cyber secure consensus of the addressed systems to determine the stability of the resulting error system, sufficient criteria including the condition in terms of LMI are derived on the basis of the Caputo fractional difference operator, by employing the Lyapunov function approach, algebraic graph theory and average dwell time (ADT). At last, the effectiveness of the obtained results is demonstrated by performing simulations on the proposed systems.

In this paper, we study the robust H∞ performance for discrete-time T-S fuzzy switched memristive stochastic neural networks with mixed time-varying delays and switching signal design. The neural network under consideration is subject to time-varying and norm bounded parameter uncertainties. Decomposing of the delay interval approach is employed in both the discrete delays and distributed delays. By constructing a proper Lyapunov-Krasovskii functional (LKF) with triple summation terms and using an improved summation inequality techniques. Sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to guarantee the considered discrete-time neural networks to be exponentially stable. Finally, numerical examples with simulation results are given to illustrate the effectiveness of the developed theoretical results.

This paper deals with the sampled-data stabilization problem for Takagi-Sugeno (T-S) fuzzy genetic regulatory networks with leakage delays. A novel Lyapunov-Krasovskii functional (LKF) is established by the non-uniform division of the delay intervals with triplex and quadruplex integral terms. Using such LKFs for constant and time-varying delay cases, new stability conditions are obtained in the T-S fuzzy framework. Based on this, a new condition for the sampled-data controller design is proposed using a linear matrix inequality representation. A numerical result is provided to show the effectiveness and potential of the developed design method.

This paper examines the problem of asymptotic stability for Markovian jump generalized neural networks with interval time-varying delays. Markovian jump parameters are modeled as a continuous-time and finite-state Markov chain. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the linear matrix inequality (LMI) formulation, new delay-dependent stability conditions are established to ascertain the mean-square asymptotic stability result of the equilibrium point. The reciprocally convex combination technique, Jensen's inequality, and the Wirtinger-based double integral inequality are used to handle single and double integral terms in the time derivative of the LKF. The developed results are represented by the LMI. The effectiveness and advantages of the new design method are explained using five numerical examples.

In this paper, the global stability problem of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks (TSFHNNs) with discrete and distributed time-varying delays is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSFHNNs with discrete and distributed time-varying delays. Here we choose a generalized Lyapunov functional and introduce a parameterized model transformation with free weighting matrices to it, in order to obtain stability region. In fact, these techniques lead to generalized and less conservative stability condition that guarantee the wide stability region. The proposed stability conditions are demonstrated with numerical examples. Comparison with other stability conditions in the literature shows our conditions are the more powerful ones to guarantee the widest stability region.