Extrinsic calibration of heterogeneous roadside sensors in IoT and smart-city settings is limited by cost, safety risk, and on-site labor, hindering large-scale rollout. We present AutoCalib-IoT, an unmanned aerial vehicle (UAV) -anchored, cross-modal online calibration framework that reframes sensor–sensor registration as sensor–world localization. A single automatically acquired high-precision semantic orthophoto provides a unified global reference. Camera extrinsics are initialized via a road-plane–induced homography to the orthophoto, while light detection and ranging (LiDAR) extrinsics are anchored using vehicle-mounted real-time kinematic (RTK) control points within the field of view. Initial parameters are obtained via a heteroscedasticity-aware 3D–3D alignment that admits a closed-form solution. During operation, cross-modal consistency scores trigger bounded, low-frequency, small-step updates that suppress slow drift, thereby avoiding heavy joint optimization and physical calibration targets. Field tests at two sites yielded rotation/translation RMSEs of 0.447°/0.352m and 0.379°/0.324m, respectively, outperforming existing methods.

This article investigates the distributed online optimization problem in a zero-sum game between two distinct time-varying multiagent networks. At each iteration, the agents not only communicate with their neighbors but also gather information about agents in the opposing network through a time-varying network, assigning weights accordingly. Moreover, we consider quantized communication and bandit feedback mechanisms, with agents transmitting quantized information and adopting one-point estimators. At each iteration, agents make and submit decisions and then receive the cost function values near their decision points rather than the full cost function information. To guarantee the payoff of each network, we design an algorithm named quantized distributed online bandit optimization in two-network (QDOBO-TN). We use dynamic Nash equilibrium regret to measure the positive payoff discrepancy between the decision sequence produced by Algorithm QDOBO-TN and the Nash equilibrium sequence. Furthermore, we propose a multiepoch version of Algorithm QDOBO-TN. The regret bounds for both algorithms are sublinear with respect to the iteration count T. Finally, we conduct a series of simulation experiments that further validate the effectiveness of the algorithms.

This article focuses on a decentralized online optimization problem over multiagent systems, where the interactions are modeled by a strongly connected directed graph. The objective of each agent is to minimize the global loss function accumulated by all agents' local loss functions, which are time-varying and only known by themselves. To address the communication bottleneck caused by the high-dimensional data and large-scale networks, we design a decentralized online algorithm with compressed communication, decentralized online gradient push-sum with compressed communication (CC-DOGPS). For strongly convex functions, a sublinear regret bound $\mathcal {O}((\ln T)^{2})$ of our designed algorithm is obtained, where $T$ is the time horizon. Finally, two numerical simulations are given to validate the theoretical results and illustrate the efficiency of our designed algorithm.

This article considers the adaptive fuzzy optimized secure self-triggered control (STC) problem for constrained fractional-order nonlinear systems (FONSs) subject to unknown false data injection attacks (FDIAs). To fulfill the unilateral full-state constraints (UFSCs), an emerging nonlinear state-dependent function with convexity is utilized by means of its property in removing the feasibility conditions existing in the traditional constraint control. Meanwhile, since the true information of the state variables under attack signals is unavailable, a method of coordinate transformation combining the compromised system and dynamic surface control is used for designing the corresponding controller while alleviating the adverse impacts of the FDIAs. Additionally, by constructing the equivalent auxiliary systems and employing actor-critic neural networks (NNs), a reinforcement learning (RL)-based adaptive fuzzy optimal program is provided to achieve optimal control. Furthermore, considering that event-triggered mechanism needs real-time supervision of the control signals, a STC strategy is introduced to circumvent this drawback and reduce communication consumption. Leveraging the fractional Lyapunov stability theory, it has been confirmed that the devised controller can ensure that the stabilization errors tend toward a small area nearby the origin at the minimum costs and all the signals arising in the closed-loop system (CLS) are bounded. Eventually, two simulation examples are offered to demonstrate the reported control algorithm’s validity.

With the popularization of communication technology, mobile devices are conveniently used to transfer and obtain information. The resulting communication network security issues are drawing more and more attention. This study is dedicated to the modeling and optimal control problems for the malware propagation between mobile devices, in which the patch-implantation and device repair are considered. A fractional network-based SVEIS (Susceptible-Vaccinated-Exposed-Infectious-Susceptible) model is developed and investigated. Different from the previous malware propagation models, the considered transmission mechanism not only includes imperfect patch-implantation and reinfection after repair, but also incorporates the impact of memory effect. The next-generation matrix method is employed to obtain a critical threshold, which denotes the mean number of newly infectious devices resulting from an infectious device in a fully susceptible mobile devices environment. The existence and stability of the steady state are analyzed correspondingly to facilitate the control of malware. The cases where multi-equilibria coexist are also given in detail. Moreover, the optimal control strategy with respect to patch-implantation and repair is designed to trade off the infectious device prevalence and control cost. The proposed model provides a benchmark for patch-implantation and repair rate in a connected mobile device system. Finally, numerical simulations are conducted to assess the model’s effectiveness while investigating the sensitivity of its parameters in the context of a Barabási-Albert (BA) scale-free network. The control effects of various strategies are evaluated through comparative experiments. And a comparison with two established models is conducted to validate the enhanced risk assessment capabilities of the proposed framework.

This article provides a novel framework for the concurrent development of asynchronous observers and controllers for discrete-time nonlinear semi-Markov jump singularly perturbed systems subjected to mismatched modes, states, and premise variables between controlled systems and observer-based controllers. Aiming at characterizing nonlinearity with parameter uncertainty, the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy technique is implemented in system modeling. Meanwhile, it is supposed that both observer and controller modes can just be acquired via a hidden Markov mode detector in the first attempt. Then, following the concept of non-parallel distribution compensation (non-PDC), the observer-based IT2 fuzzy asynchronous controllers are constructed with observers and controllers sharing the same fuzzy membership function but different from that in systems, which improves the designed flexibility. In accordance with semi-Markov kernel approach and the Lyapunov function contingent upon both system modes and sojourn times, sufficient criteria are established for the functioning of expected mode-dependent IT2 fuzzy observers and controllers such that the σ-mean-square stability for the resulting nonlinear augmented semi-Markov jump singularly perturbed systems comprised of the controlled systems and observation error systems is guaranteed. Furthermore, from the perspective of fuzzy processing, parameters and relaxation matrices that comply with fuzzy rules are added to ensure system stability while further reducing the conservatism of conditions. Ultimately, a circuit model and comparison examples are shown to substantiate the necessity and superiority of the suggested technique.

This article focuses on the online distributed optimization with privacy protection in time-varying unbalanced networks. We consider the case where there exist potential passive attackers in the network, having access to all communication channels. The attackers attempt to deduce the privacy of participating nodes. In this case, the differential privacy approach is leveraged to enable privacy protection. Based on this privacy-preserving approach and the stochastic subgradient method, a novel differentially private distributed online stochastic subgradient descent (DP-DOSSD) algorithm is devised for addressing the considered problem. Unlike the existing algorithms that require the weight matrices to be doubly stochastic, our algorithm only depends on two different weight matrices that are column-stochastic and row-stochastic, respectively. Moreover, a stochastic subgradient rescaling technique is adopted to tackle the unbalancedness of time-varying directed networks. It proves that our algorithm not only guarantees ϵ-differential privacy but also establishes an expected regret of orderO(√T) in convex settings, where ϵ andTare the privacy level and the time horizon, respectively. The established result matches the optimal regret bound derived by state-of-the-art algorithms. The fundamental tradeoff between convergence performance and privacy level is also studied. Finally, simulation results for the sensor network-based online distributed estimation problem and the distributed online ridge regression problem are provided to confirm the effectiveness of our approach.

We focus on the problem of distributed online constrained convex optimization with statistical privacy in multiagent systems. The participating agents aim to collaboratively minimize the cumulative system-wide cost while a passive adversary corrupts some of them. The passive adversary collects information from corrupted agents and attempts to estimate the private information of the uncorrupted ones. In this scenario, we adopt a correlated perturbation mechanism with globally balanced property to cover the local information of agents to enable privacy preservation. This work is the first attempt to integrate such a mechanism into the distributed online (sub)gradient descent algorithm, and then a new algorithm called privacy-preserving distributed online convex optimization (PP-DOCO) is designed. It is proved that the designed algorithm provides a statistical privacy guarantee for uncorrupted agents and achieves an expected regret in $\mathcal {O}(\sqrt {K})$ for convex cost functions, where K denotes the time horizon. Furthermore, an improved expected regret in $\mathcal {O}(\log (K))$ is derived for strongly convex cost functions. The obtained results are equivalent to the best regret scalings achieved by state-of-the-art algorithms. The privacy bound is established to describe the level of statistical privacy using the notion of Kullback-Leibler divergence (KLD). In addition, we observe that a tradeoff exists between our algorithm's expected regret and statistical privacy. Finally, the effectiveness of our algorithm is validated by simulation results.

This paper studies the exponential synchronization problem of a class of fuzzy memristive inertial neural networks (FMINNs) with time-varying delays. Considering the controller gain fluctuation and transmission delay, nonfragile memory sampled-data control is firstly employed to solve the synchronization of FMINNs. This work does not use the variable conversion technique, but directly performs efficient analysis of the system. An improved fuzzy membership functions-dependent Lyapunov-Krasovskii functional with two-sided looped-functional is designed, which is based on the entire sampling period, and includes the current states and delayed states information. Then, a fuzzy sampled-data controller with a switching topology is designed, and synchronization criteria are established, which take the excitatory and inhibitory of memristive synaptic weights into account. Finally, the effectiveness of the proposed method and the practicability of the addressed model are verified through numerical results.

This paper studies the problem of adaptive fuzzy tracking for a class of nonstrict feedback stochastic systems with input delay and asymmetric state constraints. Input delay is addressed based on the Pade approximation and introducing an intermediate variable, then the control problem for the original systems is transformed into one for non-delay system. For state constraints in the system, this paper proposes nonlinear state dependent function (NSDF) instead of barrier Lyapunov function (BLF), which removes the feasibility conditions. By fuzzy logic system (FLS) and variable separation technology, this paper effectively solves algebraic rings created by nonstrict feedback structures. A fuzzy adaptive controller is proposed to ensure that all variables in the system are bounded in probability and the asymmetric state constraints are well kept all the time. Finally, simulation examples confirm the effectiveness of control strategy.

This article focuses on the resilient fixed-time stabilization of switched neural networks (SNNs) under impulsive deception attacks. A novel theorem for the fixed-time stability of impulsive systems is established by virtue of the comparison principle. Existing fixed-time stability theorems for impulsive systems assume that the impulsive strength is not greater than 1, while the proposed theorem removes this assumption. SNNs subjected to impulsive deception attacks are modeled as impulsive systems. Some sufficient criteria are derived to ensure the stabilization of SNNs in fixed time. The estimation of the upper bound for the settling time is also given. The influence of impulsive attacks on the convergence time is discussed. A numerical example and an application to Chua's circuit system are given to demonstrate the effectiveness of the theoretical results.

This article is concerned with the distributed convex constrained optimization over a time-varying multiagent network in the non-Euclidean sense, where the bandwidth limitation of the network is considered. To save the network resources so as to reduce the communication costs, we apply an event-triggered strategy (ETS) in the information interaction of all the agents over the network. Then, an event-triggered distributed stochastic mirror descent (ET-DSMD) algorithm, which utilizes the Bregman divergence as the distance-measuring function, is presented to investigate the multiagent optimization problem subject to a convex constraint set. Moreover, we also analyze the convergence of the developed ET-DSMD algorithm. An upper bound for the convergence result of each agent is established, which is dependent on the trigger threshold. It shows that a sublinear upper bound can be guaranteed if the trigger threshold converges to zero as time goes to infinity. Finally, a distributed logistic regression example is provided to prove the feasibility of the developed ET-DSMD algorithm.

The distributed convex optimization problem over the multiagent system is considered in this article, and it is assumed that each agent possesses its own cost function and communicates with its neighbors over a sequence of time-varying directed graphs. However, due to some reasons, there exist communication delays while agents receive information from other agents, and we are going to seek the optimal value of the sum of agents’ loss functions in this case. We desire to handle this problem with the push-sum distributed dual averaging (PS-DDA) algorithm. We study the effects of communication delays on the convergence results of the PS-DDA algorithm and propose an explicit bound on the convergence rate. It is proved that this algorithm converges, and the convergence result of the PS-DDA algorithm will be worse as the maximum delay value on one edge becomes larger. Our analysis indicates that the PS-DDA algorithm can converge at a rate of${\mathcal {O}}(T^{-0.5})$with proper step size, where$T$is iteration span. We finally apply the theoretical results to numerical simulations to show the PS-DDA algorithm’s performance.

In this article, we concentrate on distributed online convex optimization problems over multiagent systems, where the communication between nodes is represented by a class of directed graphs that are time varying and uniformly strongly connected. This problem is in bandit feedback, in the sense that at each time only the cost function value at the committed point is revealed to each node. Then, nodes update their decisions by exchanging information with their neighbors only. To deal with Lipschitz continuous and strongly convex cost functions, a distributed online convex optimization algorithm that achieves sublinear individual regret for every node is developed. The algorithm is built on the algorithm called the push-sum scheme that releases the request of doubly stochastic weight matrices, and the one-point gradient estimator that requires the function value at only one point at every iteration, instead of the gradient information of loss function. The expected regret of our proposed algorithm scales as$\mathcal {O} (T^{2/3} \ln ^{2/3}(T))$, and$T$is the number of iterations. To validate the performance of the algorithm developed in this article, we give a simulation of a common numerical example.

This article investigates the event-triggered adaptive fuzzy fixed-time secure control problem for a class of nonlinear cyber-physical systems subject to unknown deception attacks and backlashlike hysteresis. Based on an improved fractional-order command filtered backstepping method, fuzzy approximation technique, and Nussbaum gain technique, a novel adaptive practical fixed-time secure control scheme is proposed. Theoretical analyses prove that the fixed-time stability of the resulting control system and boundedness of all signals in the closed-loop system can be guaranteed by using the presented resilient controller. Especially, the given convergence time is independent of the initial states of the system and better system performance consisting of higher control accuracy and faster convergence rate can be ensured. Finally, a chemical reaction system is carried out to show the validity and superiority of the developed secure control scheme.

This article considers neural network (NN)-based adaptive finite-time resilient control problem for a class of nonlinear time-delay systems with unknown fault data injection attacks and actuator faults. In the procedure of recursive design, a coordinate transformation and a modified fractional-order command-filtered (FOCF) backstepping technique are incorporated to handle the unknown false data injection attacks and overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for virtual control laws. The theoretical analysis proves that the developed resilient controller can guarantee the finite-time stability of the closed-loop system (CLS) and the stabilization errors converge to an adjustable neighborhood of zero. The foremost contributions of this work include: 1) by means of a modified FOCF technique, the adaptive resilient control problem of more general nonlinear time-delay systems with unknown cyberattacks and actuator faults is first considered; 2) different from most of the existing results, the commonly used assumptions on the sign of attack weight and prior knowledge of actuator faults are fully removed in this article. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.

This article studies the output regulation problem (ORP) for nonlinear systems based on prescribed performance control (PPC). The items with the partial derivative of the virtual controller are combined together by using backstepping, and then the fuzzy logic systems (FLSs) are used to approximate these combined items, so that the designed virtual controller does not have the partial derivative of the previous virtual controllers. Therefore, this method not only reduces the calculation burden in the backstepping method, but also avoids the disadvantages of dynamic surface control (DSC). Finally, a function$\Theta $is constructed such that the overall performance (dynamic performance and steady-state performance) of the tracking error (OPTE) is constrained by PP functions. The proposed control algorithm ensures that all the signals are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error achieves the PPC. Simulation examples are provided to illustrate the effectiveness of the proposed method.

This article studies the adaptive finite-time quantized tracking control problem for a class of full state-constrained nonlinear systems with unknown control directions based on a modified fractional-order dynamic surface control (FODSC) technique. First, fractional calculus is introduced to filter design to avoid the issue of the “explosion of complexity” exposed in the traditional backstepping technique. To facilitate the control design, barrier Lyapunov functions and Nussbaum gain technique are utilized to handle full state-constrained problem and the unknown control directions, respectively. In addition, the fuzzy logic systems are employed to approximate the unknown nonlinearity of the system. By integrating with the approximation errors and compensating signals, a composite adaptive quantized controllers is designed to guarantee all the signals of the closed-loop systems are bounded and tracking error converges to an arbitrarily small neighborhood of the zero within a finite time. Finally, a mechanical horizontal platform model and a Brusselator model are carried out to verify the effectiveness of the presented control method.

This article addresses the adaptive event-triggered nonfragile state estimation for the fractional-order complex networked systems subject to randomly occurring nonlinearities and adversarial network attacks, in which the order of fractional derivative operator satisfies$0 < p < 1$. To reduce unnecessary transmission burden as much as possible in an allowable range, an adaptive event-triggered scheme (AETS) is introduced to determine whether the data released by the sensor should be transmitted to the nonfragile state estimator. First, based on the considerations of the designed AETS and stochastic cyber-attacks, one constructs a newly fractional-order estimation error system model. Then, by employing the Lyapunov functional approach and the properties of Mittag–Leffler (M–L) functions, a sufficient condition is obtained to ensure the augmented error system stochastic mean-square stability; moreover, by making use of matrix’s singular value decomposition (SVD), the desired nonfragile state estimator is designed, and the estimator gains can be obtained by finding the feasible solutions of linear matrix inequality (LMI). Finally, a numerical example and Chua’s circuit model example are given to illustrate the feasibility of the designed nonfragile estimator.

This article develops a generalized$\mathcal {H}$-representation, which can transforms stochastic systems into deterministic systems. With the help of this new approach, the necessary and sufficient conditions of$p$th moment asymptotic stability and stabilization of linear stochastic systems are addressed. Meanwhile, the generalized$\mathcal {H}$-representation method is also applied to investigate$p$th moment observability and$p$th moment complete observability for the addressed systems.

This paper aims to investigate the event-based extended dissipative state estimation problem for memristor-based Markovian neural networks in the presence of hybrid time-varying delays and sensor nonlinearity. To tackle the effect caused by information latching, sudden interference and environmental variation, the Markov jump model is employed to describe the memristor-based neural network. Besides, an event-triggered scheme is introduced to economize the cost of communication. Then some novel conditions are presented, which guarantee that the augmented error system is stochastically stable with an extended dissipative performance. The existence criterion of the desired mode-dependent estimator is also obtained in terms of linear matrix inequalities. Finally, simulation results are provided to show the effectiveness of the proposed method.

This article investigates the adaptive output feedback resilient control problem for a class of incommensurate fractional-order (FO) nonlinear systems in the presence of time-varying delays and actuator faults by combining with an adaptive backstepping technique and a modified FO dynamic surface control (FODSC) method. Considering that the information of system states is not fully available, a hybrid fuzzy observer is designed to estimate the unmeasurable system states, where the neuro-fuzzy network system is introduced to handle the unknown nonlinear functions existing in the system. Furthermore, in order to overcome the problem of the explosion of complexity caused by traditional backstepping design procedure, an FO filter is constructed to pass the virtual control signal based on the FODSC scheme. Moreover, according to an indirect Lyapunov stability method, an adaptive hybrid fuzzy output feedback controller is designed to guarantee that all the signals of the closed-loop systems are bounded. Finally, three examples are given to verify the validity and superiority of the presented control scheme.

This article studies the adaptive backstepping control problem for a class of fractional-order (FO) nonlinear systems subject to input quantization and unknown control directions by combining with an indirect FO Lyapunov stability method, and a command filter-based FO dynamic surface control (FODSC) technique. First, a modified FODSC method is utilized to reduce the computational complexity existing in the conventional recursive procedure in which an FO command filter is designed to obtain the command signals and their FO derivatives. Furthermore, the Nussbaum function and neuro-fuzzy networks (NFNs) are adopted to deal with the problem of the unknown control directions and unknown nonlinear functions existing in the system. Moreover, by introducing the compensation and prediction mechanism to controller design, the adaptive controllers, and adaptive laws are constructed to ensure that all the signals of the controlled systems are bounded. Finally, two examples are given to show the validity of the developed control method.

This article investigates the issue of neuro-fuzzy-based adaptive dynamic surface control (DSC) for uncertain fractional-order (FO) nonlinear systems in strict-feedback form where input constraint is considered in the systems. In the recursive steps, the neuro-fuzzy network systems are employed to deal with the unknown nonlinear terms existing in systems. Furthermore, based on a DSC scheme, a modified FO filter is constructed to overcome the problem of explosion of complexity caused by the traditional backstepping design. Moreover, according to the FO Lyapunov stability theory, a neuro-fuzzy-based adaptive controller is designed to guarantee all the signals of FO closed-loop systems tend to be bounded. Finally, the three examples are provided to verify the validity and superiority of the presented control scheme.

This article considers admissibility analysis and stabilization for implicit Markovian jump systems (IMJSs) with retarded discrete-distributed delays. Admissibility analysis is investigated for the unforced delay IMJSs by virtue of reciprocally convex integral inequality technique and Barbalat’s lemma. State feedback controller is designed via the matrix transformation technique to realize the stabilization of the delayed closed-loop IMJSs. By selecting comprehensive L-K functional with modes and delays information, admissibilization conditions are presented in terms of LMIs. Two illustrative examples including an inverted pendulum controlled by a direct current motor (DCMCIP) system are utilized to certify the effectiveness and practicality of the admissibilization technique.

This paper presents an auxiliary random series approach to model the effect of network induced problems, such as data losses and transmission delay subject to event-based communication scheme for nonlinear continuous time systems. T-S fuzzy model is employed to describe the nonlinear systems. In order to save the bandwidth and energy, we introduce the event-triggered mechanism to reduce the number of data for transmission and computation. Thus, it is necessary to consider the influence of data losses, data disorder, and transmission delay since the transmitted data packets become more important. Consequently, it is very complicated to analyze the performance of such networked system and one of the most difficult part, in the authors' opinion, is to construct the mathematical model of closed-loop systems. In this paper, we present an auxiliary random series approach to describe the data transmitted in the system, and therefore, the closed-loop systems can be obtained. Associated with a tailor-made Lyapunov-Krasovskii functional, the stability analysis is processed and a fuzzy controller is designed. Asynchronous membership functions are considered to obtain more relaxed stability conditions. To clarify the effectiveness of the proposed method, a cart-damper-spring system is employed for simulation.

In this paper, we study the consensus tracking problem for high-order nonlinear uncertain multiagent systems. By using the fixed-time control technique and the modified addition of a power integrator method, a novel distributed observer-based consensus protocol is proposed. Compared with the existing results in the literature, the proposed protocol can achieve consensus tracking in a fixed time independent of initial conditions even in the presence of unknown parameters and nonlinear uncertainties bounded by positive functions. Simulation examples are given to illustrate the effectiveness of the theoretical results.

This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptive control technique is combined with the sign function to deal with the unknown control gain. Then, with the help of the radial basis function neural network approximation approach and Lyapunov-Krasovskii functional, an adaptive state-feedback controller is obtained through the backstepping design procedure. It is verified that the constructed controller can render the closed-loop system semiglobally uniformly ultimately bounded. Finally, both the practical and numerical examples are presented to validate the effectiveness of the proposed scheme.

This paper aims to design a dissipative fuzzy filter for a class of discrete-time nonlinear networked systems. In order to adopt the limited communication links, we employ an eventtriggered scheme to reduce the number of transmitted data in the network by preventing the unnecessary ones from releasing. Due to the digital channel, the data to be transmitted should be quantized and a logarithmic quantizer is employed. Then, when the part of released data is transmitted in the network, data losses is captured by a Bernoulli process. Consequently, the uncomplete data sequence is compensated by the buffer and then send to the filter. During this process, a new random series is developed to help constructing the filtering systems. Thus, a novel method is presented to guarantee the filter error system to be dissipative based on the T-S fuzzy model approach. Finally, an example concerned with Henon mapping system is provided to verify the validity of the proposed design method.

This paper studies the resilient asynchronous H∞control problem for slow sampling discrete-time uncertain Markov jump singularly perturbed systems (SPSs). Compared with slow state variables feedback controller, a new controller is proposed for slow sampling discrete-time SPSs, which has less conservatism. A more realistic situation, i.e., the system modes cannot be directly acquired for controller design, is considered with the help of hidden Markov model (HMM). The goal is to design a resilient asynchronous controller based on HMM such that the closed-loop system is stochastically stable while meeting an expected H∞performance in the presence of random controller gain fluctuation and the system modes hiding for controller. By utilizing matrix inequality techniques and Lyapunov function method, some criteria are established for the existence of the resilient asynchronous controller. The superiority and practicability of the obtained theoretical results are demonstrated by a numerical example and an inverted pendulum system.

This paper is concerned with the practically finite-time (PFT) control problem for a class of more general nonlinear systems subject to mismatching time-varying disturbances. Without any extra assumptions on system nonlinearities, a composite controller is developed by introducing a disturbance observer and finite-time control technique. Based on the designed controller and Lyapunov stability theory, the PFT stability of the closed-loop system is strictly verified and proven to be a better convergence performance. Furthermore, as a byproduct of the proposed design method, the disturbance observer-based PFT control for high-order nonlinear systems is also shown to be possible. To the best of the authors' knowledge, it is the first PFT control result for high-order nonlinear systems with external disturbances. Finally, an application example of a singlelink robot system with disturbances and a numerical example are presented to demonstrate the effectiveness of the proposed scheme, respectively.

A fractional-order integral fuzzy sliding mode control scheme is proposed for a class of uncertain fractional order nonlinear systems subject to uncertainties and external disturbances. First, in each step, a neuro-fuzzy network system is developed to approximate the uncertain nonlinear function existing in fractional-subsystem and a fractional sliding mode surface is presented. Second, based on the fractional Lyapunov stability theory and the finite-time stability theory, a fractional adaptive backstepping neuro-fuzzy sliding mode controller is designed to drive the state trajectories of fractional-order systems to the prescribed sliding mode surface. Meanwhile, the finite-time stability of the fractional-order closed-loop system is proved. At last, three numerical examples are given to illustrate the effectiveness of the proposed control method.

This paper is concerned with the state-feedback stabilization for singular Markovian jump systems with distributed delays. Firstly, delay-dependent admissibility conditions are obtained by employing a mode-dependent Lyapunov-Krasovskii functional and applying the delay-partitioning technique. Secondly, strict LMI-based conditions for solving the stabilization problem are presented, based on which the desired controller gains can be determined. Finally, a numerical example is given to illustrate the effectiveness of the proposed methods.

In this paper, an adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems. Backstepping design with a tan-type barrier Lyapunov function is proposed. Meanwhile, fuzzy logic systems are used to approximate the unknown nonlinear functions. The addressed control scheme guarantees that the output is followed the reference signals within a bounded error, and all the signals in the closed-loop system are bounded. The simulation results demonstrate the validity of the proposed method.

This paper is concerned with the L1-gain filtering problem of positive linear continuous-time systems in which the signals are transmitted through event-triggered network communication channels. A network-based filter system model is proposed to estimate the variables of a positive system. The event-triggered communication scheme in a linear form is presented to cut down the amount of the data in transmission, and save the limited communication bandwidth, as well. By constructing an augmented filtering error system and using the linear Lyapunov method, a sufficient condition to ensure the existence of the L1-gain filter is derived. In addition, a linear programming approach to the filter design is proposed through which the filter parameters can be obtained. A numerical example is presented to illustrate the theoretical results.

This paper investigates the cooperative output regulation problem of singular heterogeneous multiagent systems. General distributed observers are proposed for every agent obtaining the estimated state of the exosystem. The feedforward control technique and reduced-order approach are used to design distributed singular output feedback controllers and distributed normal output feedback controllers. The proposed cooperative dynamic controller is dependent on the plant parameters and the interaction topologies. A simulation example is provided to demonstrate the effectiveness of the proposed design method.

This paper focuses on the event-triggered network-based synchronization problem for a class of complex networks with delayed couplings. The dynamic of each node in the considered networks is described by a neutral neural network. The remote master and the complex networks transmit signals mutually through the common communication channels, which include network-induced delays and stochastic fluctuations. An event-triggering scheme is proposed and utilized when the controller transfer signals to the complex networks. A state error system is formulated in which the event-triggering scheme is concerned with. By using Lyapunov method and some properties of Kronecker product, some synchronization criteria are derived to ensure the global mean-square exponential synchronization of state trajectories of the remote master and the coupled dynamic networks. The event-triggered controller design method is further proposed.

This paper revisits the problem of asymptotic stability analysis for neural networks with distributed delays. The distributed delays are assumed to be constant and prescribed. Since a positive-definite quadratic functional does not necessarily require all the involved symmetric matrices to be positive definite, it is important for constructing relaxed Lyapunov-Krasovskii functionals, which generally lead to less conservative stability criteria. Based on this fact and using two kinds of integral inequalities, a new delay-dependent condition is obtained, which ensures that the distributed delay neural network under consideration is globally asymptotically stable. This stability criterion is then improved by applying the delay partitioning technique. Two numerical examples are provided to demonstrate the advantage of the presented stability criteria.

This paper is concerned with the H∞tracking control problem for linear time-delay systems with Markovian jumping parameters. The objective is to design state-feedback mode-dependent controllers such that the resulting closed-loop system is stochastically stable and its state follows a reference signal. Based on the Lyapunov-Krasovskii functional method together with the delay-partitioning technique, delay-dependent conditions for the existence of the desired controllers are obtained in terms of linear matrix inequalities. In addition, a new stability criterion for Markovian jump systems with constant delays is also given, which is less conservative than the recent ones in the literature. Numerical examples are provided to demonstrate the effectiveness of the proposed methods.

The problem of designing robust H∞filters for Markovian jump systems with time-varying delays and parametric uncertainties is studied. Some delay-dependent conditions for the existence of the desired H∞filter are obtained via a new mode-dependent Lyapunov functional such that the corresponding filtering error system is robustly stochastically stable with a prescribed H∞performance level. The attractive feature of the derived conditions is that they are expressed in terms of strict linear matrix inequalities, thus being easily solvable.

This paper considers the problem of stability analysis for uncertain stochastic neural networks with distributed delays and reaction-diffusions. Two sufficient conditions for the robust exponential stability in the mean square of the given network are developed by using a Lyapunov-Krasovskii functional, an integral inequality, and some analysis techniques. The conditions, which are expressed by linear matrix inequalities, can be easily checked. Two simulation examples are given to demonstrate the reduced conservatism of the proposed conditions.

In this paper the problem of passivity analysis is investigated for Markovian jump time-delay systems subject to norm-bounded parametric uncertainties. A mode-dependent Lyapunov functional is introduced together with some slack variables to derive new delay-dependent conditions that guarantee the passivity of uncertain Markovian jump time-delay systems. The derived passivity conditions in the form of linear matrix inequalities not only are readily checkable but also are shown to be less conservative than the existing results. The theoretical predications are justified by an illustrative example.

This paper deals with the robustHinfincontrol problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with norm-bounded parametric uncertainties and interval time-varying delays. First, based on a new Lyapunov functional, we present a sufficient condition guaranteeing that the resulting closed-loop system is robustly stable and satisfies a prescribedHinfinperformance level. The Lyapunov functional used here depends on not only the fuzzy basis function but on the lower and upper bounds of the time-varying delay as well. Second, two classes of delay-dependent conditions for the existence of the concernedHinfinfuzzy controllers are given in terms of relaxed linear matrix inequalities (LMIs), and a desired controller can be designed by using the solutions to these LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed design method.

This paper provides a delay-dependent approach to the design of fuzzy dynamic output feedback controllers for uncertain discrete-time fuzzy Markovian jump systems with interval time-varying delays. First, by a fuzzy-basis-dependent and mode-dependent Lyapunov functional, a stochastic stability condition is derived by using the Finsler's lemma. Second, in terms of linear matrix inequalities (LMIs), a delay-dependent sufficient condition is presented, under which there exists a fuzzy output feedback controller such that the resulting closed-loop system is robustly stochastically stable. A desired controller can be constructed when these LMIs are feasible. Finally, the effectiveness of the proposed design method is demonstrated by a simulation example.

This paper considers the problem of robust exponential stability for a class of recurrent neural networks with time-varying delays and parameter uncertainties. The time delays are not necessarily differentiable and the uncertainties are assumed to be time-varying but norm-bounded. Sufficient conditions, which guarantee that the concerned uncertain delayed neural network is robustly, globally, exponentially stable for all admissible parameter uncertainties, are obtained under a weak assumption on the neuron activation functions. These conditions are dependent on the size of the time delay and expressed in terms of linear matrix inequalities. Numerical examples are provided to demonstrate the effectiveness and less conservatism of the proposed stability results.

This paper investigates the problem of stability analysis for recurrent neural networks with time-varying delays and polytopic uncertainties. Parameter-dependent Lypaunov functionals are employed to obtain sufficient conditions that guarantee the robust global exponential stability of the equilibrium point of the considered neural network. The derived stability criteria are expressed in terms of a set of relaxed linear matrix inequalities, which can be easily tested by using commercially available software. Two numerical examples are provided to demonstrate the effectiveness of the proposed results.