This article investigates the mean square exponential stability for dynamic memristor-neutral stochastic cellular neural networks with time-varying delays (DM-NSDCNNs). Unlike general neural networks (NNs) analyzed in the voltage-current domain, DM-NSDCNNs are studied in the flux-charge domain, offering a significant advantage: all current, voltage, and power consumption vanish when the system reaches a steady state. In particular, dynamic memristor store the results of computation. To better utilize these properties, two distinct stochastic stability analysis techniques are considered, depending on the memristor's constitutive relations. For piecewise linear constitutive relation, the stability criteria are obtained by a novel approach based on the comparison principle and reductio ad absurdum. Moreover, the stability criteria for cubic nonlinear constitutive relation are established via stochastic analysis employing Lyapunov functional techniques. Finally, several numerical examples with different constitutive relations of DM-NSDCNNs are provided to verify the effectiveness and potential of the proposed results.

This article investigates iterative learning control of robot arms with initial errors and actuator faults. Resorting to backstepping technique, an actual controller that removes the iterative convergemt sequence is constructed to simplify the design. New updating laws with equilibrium factor are developed to treat the nonstrictnegative difference of composite energy function. A contraction mapping-based composite energy function method is executed to provide the convergence of errors. Finally, the validity of the proposed method is verified through a robot arm example.

This article investigates finite/fixed-time synchronization (FTS/FXTS) for delayed inertial memristive neural networks (IMNNs) with time-varying actuator faults, a more practical scenario compared to the widely studied constant-fault case. Novel fault-tolerant controllers, including state-feedback and event-triggered schemes, are proposed to achieve synchronization under mixed delays. By establishing algebraic criteria via a nonreduced-order approach, explicit settling time estimates are derived while excluding Zeno behavior. The theoretical results are verified through simulations, and the proposed method is further applied to secure communication using synchronized IMNNs for image encryption.

This article is devoted to data-driven optimal control of linear discrete systems with sensor fault via a performance triggering approach. A quadratic inequality is introduced to equivalently describe systems subject to a fault. A barrier function is provided by the input and output to treat the gain constraint. An optimal control law is built on the adaptive dynamic programming (ADP) method to improve control performance. A dynamic triggering mechanism is constructed by instantaneous data and performance index to balance triggering frequency and control performance, especially under an emergent situation. Sufficient conditions are supplied to ensure the ultimately uniform boundedness of the closed-loop systems. An illustrative example is presented to verify the validity of the proposed strategy.

This paper is dedicated to a dynamic event-triggeredH∞filtering method of fuzzy Markov jump systems via a mismatched quantization scheme. The system outputs are triggered by a dynamic event-triggered mechanism and then quantized via a mismatched quantizer before being sent to the remote filter. The dynamic triggering scheme with a special diagonal matrix structure threshold is built to reduce the network burden. The quantizer is constructed in a multi-channel paradigm with a time-varying mismatch degree. Then, the remote reduce-order filter is designed to be both fuzzy-rule and mode-dependent. By adopting Finsler's Lemma and the vertex separation method, sufficient conditions are derived in terms of form matrix inequalities. At last, the effectiveness of the proposed method is demonstrated by a tunnel diode circuit.

The paper addresses the fault-tolerant optimized control of switched complex networks with unknown state and actuator fault. A proportional-integral intermediate observer is constructed to estimate unknown elements by relaxing known boundary requirement. An optimized controller is proposed to achieve fault-tolerant synchronization via adaptive dynamic programming scheme. A critic neural network is employed to solve the value of the Hamilton–Jacobi–Bellman equation. Sufficient conditions are established to ensure the uniformly ultimately bounded synchronization performance. Finally, an example is simulated to deliver the effectiveness of the proposed approach.

This paper is concerned with the intermittent iterative learning control for robot manipulators with packet dropouts. A composite controller is presented by a proportional-derivative feedback and an iterative learning feedforward to deal with the dropouts. A modified reference trajectory is adopted to treat input intermittence. With the help of composite energy function, rigorous theoretical derivations are provided to ensure the convergence of the estimation error of the iterative estimator and the boundedness of the tracking error. The validity of the proposed method is demonstrated by simulation studies on a single-link manipulator and a two-degrees-of-freedom planar manipulator, respectively. Note to Practitioners—Robot manipulators have drawn significant attentions due to their wide industrial applications. Due to their repetitive feature, iterative learning control is an effective strategy to achieve high tracking accuracy for them. However, this strategy faces new challenges incurred from network environment, such as packet dropouts and how to save energy. To patch this gap, an interpolation method is exploited for an iterative estimator to handle packet dropouts and a modified reference trajectory is employed to tackle with input intermittence. Simulation studies with practical background is supplied to show the validity of the proposed method. Therefore, this paper lays a theoretical basis for the tracking control of robot manipulators in practical applications.

This paper focuses on the issue of limited information based emergency response control for a specific category of underwater cyber-physical systems (UCPSs) confronted with multiple emergencies. The focus is on a typical underwater vehicle system (UVS), whose communication with the control center may be compromised by Denial-of-Service (DoS) attacks. The occurrence of dual DoS attacks leads to significant information loss, intensifying the complexity of decision-making and emergency response during critical situations. Moreover, abrupt ocean current disturbances or faults will also seriously affect the performance of UVSs. Drawing upon distinct DoS attack scenarios, this study introduces a novel emergency response decision mechanism. A limited information-based disturbance observer (DO) is then proposed to effectively handle various unknown disturbances, ensuring favorable disturbance estimation performance. Subsequently, as for different attack channels, two innovative emergency response controllers are respectively proposed, considering the constraints of limited information. Furthermore, distinct stability criteria are derived by using Lyapunov stability and stochastic analysis techniques to guarantee the stabilization of UVSs. Finally, a series of numerical results is presented to illstrate the efficiency of the proposed algorithm under various emergency scenarios.Note to Practitioners—This study presents an novel framework for emergency automatic control and decision-making in UVSs suffering from different emergencies. While most of previous research primarily concentrated on emergency situations in the physical layer, this article delves into both the information layer and the physical layer to tackle decision-making and response challenges within information-constrained environments. Specifically, a limited information-based DO is designed to dynamically model disturbances, such as ocean currents and actuator faults. The DO adapts its structure based on valuable information obtained from emergency monitoring, ensuring accurate disturbance estimation upon successful transmission. The proposed strategy holds significant practical applicability in UVSs for handling emergencies, and further experimental validations are planned to be conducted on actual underwater vehicles.

This paper mainly analyzes the finite-time stabilization of semi-Markov reaction-diffusion memristive neural networks (R-DMNNs) with unbounded time-varying delays. Firstly, the reaction-diffusion term and semi-Markov jumping are introduced into memristive neural networks, which relaxes the limitation of Markov switching on sojourn time and makes the model more applicable. Secondly, by constructing a suitable comparison function, the states of R-DMNNs converges to 0 directly, which can clearly estimate the upper limit of the settling time and simplify the complexity of the theoretical derivation. Furthermore, this paper removes the requirement of bounded and differentiable time delay, which provides a new perspective for understanding the finite-time stabilization of the neural networks with reaction-diffusion terms. Finally, one example illustrates the usefulness of the analysis results in this research.

This article addresses synchronization control of switched complex network with unknown state and actuator fault. A mode-dependent proportional-integral intermediate observer is explored to estimate unknown elements with high-estimation accuracy. A hybrid controller is constructed to treat the asynchronous occurrence of impulses and switching moments. With the help of mode-dependent average dwell time and mode-dependent average impulsive interval, a mode-dependent criterion is established to guarantee the uniformly bounded synchronization performance. Two examples are simulated to deliver the effectiveness of the proposed method.

Neural network adaptive iterative learning control (ILC) is developed in this article to treat strict-feedback nonlinear systems with unknown state delays and input saturation. These delays are treated by constructing the Lyapunov-Krasovskii (L-K) functions for each subsystem. A command filter is employed to avoid the derivative explosion caused by continuous differentiation of the virtual controller. Corresponding auxiliary systems are designed and integrated into the backstepping procedure to compensate input saturation and the unimplemented part of the filter. Hyperbolic tangent functions and radial basis function neural networks (RBF NNs) are employed to treat singularity and related unknown terms, respectively. The convergence of the resultant strict-feedback systems is ensured in the framework of composite energy function (CEF). Finally, a simulation example is adopted to substantiate the validity of the proposed algorithm.

Robust resilient control is promising and effective since it can combat gain perturbations in control system design. In this article, finite-time resilient control is investigated for the networked Markov switched nonlinear systems (NMSNSs). Both additive and multiplicative feedback gain perturbations with randomly occurring manners are considered during the controller design to improve its tolerance of inaccurate controller implementation. The mode information that is partially available to the controller is incorporated into the design. With the help of the fuzzy-logic method, fuzzy resilient output-feedback controllers are established to cope with the gain perturbations and immeasurable system states. Then, by developing a novel switching model, relaxed conditions compared with the existing results are obtained which guarantee the finite-time boundedness (FTB) of NMSNSs. Based on the FTB analysis, a fuzzy design algorithm is proposed via a new separation approach to obtain the controller gains. Eventually, simulations are conducted via a multimode robotic arm system to validate the achieved results.

This article is dedicated to a fault-tolerant synchronization control method of complex networks (CNs) via an average observer. A projected operator matrix is constructed to reduce the dimension of CNs by clustering and aggregation. An average observer with derivative and integral terms is built to estimate unknown elements with high estimation accuracy. The fault-tolerant controller is a composite form of state-feedback and the estimated faults. Sufficient criterions are set up to guarantee the uniformly ultimately bounded synchronization of the resultant close-loop system. Finally, an example is simulated to deliver the effectiveness of the proposed approach.

This article aims to study event-triggered data-driven control of nonlinear systems via Q-learning. An input-output mapping is described by a pseudo-partial derivatives form. A Q-learning-based optimization criterion is provided to establish a data-driven control law. A dynamic penalty factor composed of tracking errors is supplied to accelerate errors convergence. Consequently, a novel triggering rule related to this factor and performance cost is proposed to save communication resources. Sufficient conditions are developed for guaranteeing the ultimately uniform boundedness of the resultant tracking errors system. Two simulation studies are executed to verify the effectiveness of the presented scheme.

This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

This article investigates the distributed robust fault estimation for a class of multiagent systems with actuator faults and nonlinear uncertainties. To estimate the actuator faults and system states simultaneously, a novel transition variable estimator is constructed. Compared with existing similar results, the fault estimator existing condition is not necessary for designing the transition variable estimator. Furthermore, the bounds of the faults and their derivatives can be unknown in designing the estimator for each agent in the system. The parameters of the estimator are calculated by using Schur decomposition and linear matrix inequality algorithm. Finally, the performance of the proposed method is demonstrated through experiments of wheeled mobile robots.

By using the fault-tolerant control method, the synchronization of memristive neural networks (MNNs) subjected to multiple actuator failures is investigated in this article. The considered actuator failures include the effectiveness failure and the lock-in-place failure, which are different from previous results. First of all, the mathematical expression of the control inputs in the considered system is given by introducing the models of the above two types of actuator failures. Following, two classes of synchronization strategies, which are state feedback control strategies and event-triggered control strategies, are proposed by using some inequality techniques and Lyapunov stability theories. The designed controllers can, respectively, guarantee the realization of synchronizations of the global exponential, the finite-time and the fixed-time for the MNNs by selecting different parameter conditions. Then the estimations of settling times of provided synchronization schemes are computed and the Zeno phenomenon of proposed event-triggered strategies is explicitly excluded. Finally, two experiments are conducted to confirm the availability of given synchronization strategies.

This article proposes the two-layer asynchronous control scheme for a class of networked nonlinear jump systems. For the constructed system in a network environment, the data transmission may suffer from many restrictions, such as incomplete acceptable mode information and transition information, nonlinearity of system and inadequate bandwidth resources, etc. Then, the two-layer asynchronous controller is developed to stabilize the plant constructed by Takagi-Sugeno (T-S) fuzzy method and semi-Markov theory (SMT). Herein, the hidden semi-Markov process with time-varying emission probability is introduced to establish the relation between the system modes and the controller modes, in which the interval segmentation method is presented to deal with this time-varying probability. Compared with some published results, this method can make full use of the transition rate information, which may lead to the reduction of conservatism in the proposed asynchronous control design. At the same time, the limited bandwidth problem in the communication channel is addressed by introducing the bilateral quantization strategy, and the new sufficient conditions are derived on the stochastic stability of the nonlinear jump system with/without incomplete transition and sojourn-time information. Finally, the numerical simulation examples about DC motor illustrate the effectiveness and the feasibility of the proposed approach.

As quaternion-valued memristor neural networks (MNNs) have important applications in many engineering fields and the information in the real world is often uncertain and inaccurate, therefore, in order to better apply to practice, it is necessary to study the dynamic behaviors of quaternion-valued MNNs with fuzzy logic. In this article, the event-based synchronization problem for quaternion-valued Takagi–Sugeno (T-S) fuzzy MNNs (QVT-SFMNNs) with time-varying delays is studied. Different from the existing synchronization results of MNNs, T-S fuzzy logic and quaternion are simultaneously considered in the MNNs, which makes the model more applicable. Based on some algebraic properties of quaternions, a novel event-based fuzzy controller and some static and dynamic event trigger conditions are designed. By constructing simple Lyapunov functions instead of complex Lyapunov functionals and using Halanay inequality, several sufficient criteria are given to ensure the global exponential synchronization between the considered QVT-SFMNNs. Meanwhile, the Zeno behaviors of the response QVT-SFMNN under different event trigger conditions are excluded. It is important to note that the results obtained in article are less conservative and applicable to low-dimensional complex-valued and real-valued MNNs. Finally, a numerical example is given to verify the validity of the obtained theoretical results.

In this article, we investigate an improved dynamic guaranteed cost event-triggered-based anti-disturbance control for Takagi–Sugeno fuzzy wind-turbine systems subject to external disturbances. A guaranteed cost event-triggered paradigm with dynamic threshold and sector structure is constructed to alleviate unnecessary triggers caused by outlier measurement. An additional event condition is designed to deal with the difference of premise variable between the system and controller. A PI-type intermediate estimator is introduced to simultaneously estimate the system state and external disturbance. Subsequently, an event-triggered fuzzy controller is built to actively compensate the external disturbances. With the help of Finsler's lemma, sufficient criteria are derived in terms of linear matrix inequalities to make the wind-turbine systems asymptotically stable. Finally, the proposed method is verified by comparative studies.

This article is concerned with the mismatched quantized$H_{\infty }$output-feedback control of fuzzy Markov jump systems via a dynamic guaranteed cost triggering scheme. An event generator and a quantizer are set up at the sensor-to-controller side and the controller-to-actuator side, respectively. The quantization scheme is presented in terms of a multichannel configuration with different decoder/encoder parameters. A guaranteed cost dynamic event-triggered mechanism is built on instantaneous and averaged triggering errors, output cost, and preset bounds. A composite controller consisting of a static output-feedback and a nonlinear compensation is constructed to meet the desired system performance. Based on the Lyapunov stability theory, sufficient conditions are obtained such that the closed-loop system is stochastically stable with the prescribed$H_{\infty }$performance. A structural vertex separation technique and Finsler's Lemma are employed to decouple the control gain, the quantizer parameters, and the Lyapunov variable. Finally, the validity of proposed scheme is verified by a circuit example.

In this article, a distributed resilient state estimation scheme is developed for nonlinear systems under sensor attacks. The scheme is implemented over a multiagent network where each agent utilizes the estimates of local observable system states and the information transmitted from its neighbors to construct a distributed median solver so that the median value of local estimates can be approximately recovered. In order to obtain the estimates of local observable states, the nonlinear function is decomposed and an unknown input observer is designed. Then, by using distributed median solver and differential mean value theorem, the secure state estimation is achieved in a fully distributed way, neither a majority of sensing data nor global information is needed, which is different from the existing results. Finally, simulation results are provided to verify the effectiveness of the proposed method.

This paper investigates the fusion-based event-triggered$H_\infty$state estimation of autonomous surface vehicles (ASVs) against measurement outliers and cyber-attacks. To release communication burden, a novel fusion-based event-triggered mechanism (FETM) dependent on the fusion of historical system outputs is proposed. There exist two advantages of this mechanism: 1) the use of fusion signal is able to avoid the information loss between two sampling instants and reduce the redundant triggering events resulted from system disturbances and noises; 2) by requiring the error signal in the triggering condition not only lager than a lower threshold but also less than an upper threshold, the false triggering events incurred by measurement outliers also can be discarded. Then, a time-varying delay system is established to represent the event-triggered$H_\infty$state estimation error system with network-induced delays. Then, sufficient conditions are deduced for solving$H_{\infty}$estimator gain and triggering matrix of FETM. Lastly, some simulation results are given to illustrate the merits of the theoretical method.

This article concentrates on the global exponential synchronization problem of multiple neural networks with time delay by the event-based output quantized coupling control method. In order to reduce the signal transmission cost and avoid the difficulty of obtaining the systems' full states, this article adopts the event-triggered control and output quantized control. A new dynamic event-triggered mechanism is designed, in which the control parameters are time-varying functions. Under weakened coupling matrix conditions, by using a Halanay-type inequality, some simple and easily verified sufficient conditions to ensure the exponential synchronization of multiple neural networks are presented. Moreover, the Zeno behaviors of the system are excluded. Some numerical examples are given to verify the effectiveness of the theoretical analysis in this article.

This article is devoted to analyzing the multistability and robustness of competitive neural networks (NNs) with time-varying delays. Based on the geometrical structure of activation functions, some sufficient conditions are proposed to ascertain the coexistence of equilibrium points, of them are locally exponentially stable, where represents a dimension of system and is the parameter related to activation functions. The derived stability results not only involve exponential stability but also include power stability and logarithmical stability. In addition, the robustness of stable equilibrium points is discussed in the presence of perturbations. Compared with previous papers, the conclusions proposed in this article are easy to verify and enrich the existing stability theories of competitive NNs. Finally, numerical examples are provided to support theoretical results.

This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

This article is dedicated to data-driven control of networked nonlinear systems with event-triggered output. An improved extended state observer is constructed to estimate unknown disturbances. An output estimator is built on the triggered output and the estimated output. Consequently, triggering conditions for single-input single-output and multiple-inputs and multiple-outputs systems are individually proposed by integrating the estimated disturbances, the true and the estimated tracking errors. Sufficient conditions are established to guarantee that the resultant tracking error systems are uniformly ultimately bounded. The proposed strategies are verified by illustrative numerical examples.

This article investigates fault detection (FD) for discrete-time T–S fuzzy systems via an iterative interval estimation method. By means of system output and the iterative estimation of unknown disturbances, two iterative subsystems are employed to establish iterative state reconstruction free of faults. Resorting to a structure separation technique and the$H_{\infty }$requirement imposed on estimated errors, a sufficient condition is formulated in terms of linear matrix inequality to guarantee the asymptotically stability of the error systems. With the help of the zonotope reachability technique, the state interval without faults consideration is rebuilt in terms of the error boundary. Subsequently, an FD scheme is proposed by checking residual signals whether exceed the residual interval generated from the established error interval. Simulation comparison is provided to verify the validity of the proposed iterative FD scheme.

This article is devoted to the fuzzy fault-tolerant tracking control of Markov jump systems with unknown mismatched faults. To reconstruct the faults and system states, a sequence of proportional–integral observers are established via the system outputs. With the help of a structure separation technique, the proportional–integral gains and the observer gains are solved by a unified linear matrix inequality framework. Resorting to the rebuilt faults and states from an iterative estimation algorithm, a backstepping-based fuzzy fault-tolerant tracking control scheme against the mismatched faults is established to make the resultant closed-loop system be uniformly ultimately bounded. Simulations are provided to verify the effectiveness of the proposed methods.

This article is concerned with the anti-disturbance control for Markov jump systems with matched and mismatched disturbances. First, the matched part is estimated by an output-based disturbance observer. Meanwhile, the mismatched part is attenuated by the$H_{\infty }$controller. Therefore, a composite static output control scheme is built to make the closed-loop system stable with the required$H_{\infty }$performance. Second, an integral sliding-mode output (ISMO) control approach is proposed to restrain the mentioned disturbances by using the upper bounds of disturbances. Third, an ISMO anti-disturbance strategy is established to integrate the advantages of the above two methods. It is noteworthy to point out that the proposed output observer structure could be reduced to the state case. Moreover, the diagonal constraint on Lyapunov variables is removed in this article. Finally, a numerical example is simulated to validate the effectiveness of the proposed methods.

This paper presents the event-triggered control for Takagi-Sugeno (T-S) fuzzy networked systems with transmission delay. An integral-based model is proposed for designing a new event-triggered scheme, which relies on the mean of the system state and the last triggered state. To handle the asynchronous premises of the fuzzy system and fuzzy controller, a novel triggering condition is added into the event-triggered mechanism. Then, the closed-loop T-S fuzzy event-triggered control system is established as a distributed delay system. With the help of the Legendre polynomials and their properties, the co-design conditions of triggering parameters and controller gains are given in linear matrix inequalities to ensure the asymptotic stability of the resulting closed-loop system. Finally, an experiment via a practical wireless network is implemented to illustrate the effectiveness of the proposed approach.

Incorporating the disturbance frequency into system analysis and synthesis, this paper is dedicated to the quantized${H_\infty }$static output control of linear Markov jump systems. The output quantization is transformed into a sector bound form, and the finite frequency performance is handled by Parseval’s theorem. With the aid of Finsler’s lemma, sufficient conditions for the resulting closed-loop system are first established to satisfy the required finite frequency performance. To treat the static output feedback control problem in the framework of linear matrix inequalities, a new strategy is developed to decompose the coupling among Lyapunov variables, controller gain, and system matrices. In contrast to the existing results in the literature, no additional assumptions are imposed on the system matrices. Numerical examples are presented to demonstrate the validity of the established results.

This paper is devoted to the control of Markov jump nonlinear systems with general transition probabilities (TPs) allowed to be known, uncertain, and unknown. With the help of the S-procedure to dispose the system nonlinearities and the TP property to eliminate the coupling between unknown TP and Lyapunov variable, an extended bounded real lemma for the considered system to be stochastically stable with the prescribed H∞ performance is established in the framework of linear matrix inequalities. To handle the nonlinearity incurred by uncertain TP for controller synthesis, a separated method is proposed to decouple the interconnection between Lyapunov variables and controller gains. A numerical example is given to show the effectiveness of the proposed method.

This paper concerns the finite‐time H ∞ filtering of discrete Markov jump system with incomplete transition probabilities which cover the cases of known, uncertain and unknown. To include all possible cases, with the probability viewpoint, a truncated Gaussian distribution is employed to describe them. To ensure the filtering error systems to be finite‐time stochastic stable with a prescribed noise attenuation level, sufficient conditions for the H ∞ filter design are yielded in terms of solvability of a set of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed method.