This study attempts to address the angular velocity regulation problem associated with quadcopters under constraints such as model-plant mismatch, uncertain loads, and limited measurement capability. The proposed solution is an output-feedback structure that includes an angular velocity observer, active damping, and a disturbance observer to enhance the performance of the closed-loop system, which provides the following features: first, the model-free observer yields estimates of angular velocities by stabilizing the diagonalized error dynamics; second, the output-feedback double integral control with feed-forward compensation terms stabilizes the angular velocity errors while satisfying a predetermined critically damped performance. An experimental platform provided by Quanser demonstrates the effectiveness of the proposed technique.

This paper proposes an observer-based output feedback controller that enforces triply damped dynamics in servo drives. This makes transients softer as well as the model less dependent. The primary contributions are as follows: (a) The observer gain, structured by virtual damping (VD) and a convergence rate, enables the estimation loop to be governed by a first-order transfer function without requiring any system model information; (b) a high-order disturbance observer (HODOB) exponentially estimates high-frequency disturbances in accordance with the triple-damping transfer function; and (c) a proportional–integral-type controller based on the observer and HODOB, incorporating VD terms, guarantees exponential recovery of the desired triply damped performance. Experimental results on a commercial servo drive confirm the practical effectiveness of the proposed method across varying load conditions.

In recent years, with the wide application of artificial neural networks (ANNs) in the field of artificial intelligence, the study of neural network bifurcation dynamics has received much attention and has achieved a large number of results, but the current research only focuses on the binary interactions between neurons and does not take into account the higher-order interactions (HOIs) that exist between neurons. Furthermore, most neural network models focus on ring, star and chain structures, but it is more practical to study multi-layer neural networks. For this reason, this paper develops a three-layer neural network with multiple time delays under HOIs. Firstly, the characteristic equation of network is obtained, and the time delay is selected as the bifurcation parameter. Subsequently, the stability of the neural network and the sufficient condition for the occurrence of Hopf bifurcation are established. Then, the correctness of the theoretical results is verified through numerical simulations. The simulation results demonstrate that the increase of the time delay leads to Hopf bifurcation, which consequently leads to the system oscillation and instability. In addition, the stability of the network is closely related to the higher-order coupling coefficient, self-feedback coefficient and unified connection weight. An increase in the higher-order coupling coefficient expands the stability domain of the network, an increase in the self-feedback coefficient enlarges the stability domain of the network, and an increase in the unified connection weight narrows the stability domain of the network. Finally, our model performs well in terms of function fitting, which also provides some insights into the subsequent modelling and analysis of higher-order neural networks.

In this paper, a new data-based Q-learning algorithm is proposed to address the optimal control issue for a class of discrete-time switched affine systems (SASs). The algorithm shifts the emphasis onto learning the optimal switching law directly from system input-output data, employing a neural-network-approximated Q-function as the key learning element. Firstly, the optimal control issue is transformed into solving the corresponding Bellman’s optimality equation based on the Q-function. Then, a new Q-learning algorithm is developed to find the optimal solution of system switching based entirely on the system input-output data, and a fully connected neural network is borrowed as the Q-function approximator. Moreover, considering the affine properties of SASs, the sequence of Q-functions generated remains bounded in proximity to the precise optimal solution. Finally, both the advantage and effectiveness of the proposed Q-learning based optimal control approach are verified by three examples, including a case study of DC-DC buck-boost converter.

With the rapid growth in electromagnetic device quantities, various forms of communication interference have emerged, significantly impacting the accuracy of signal classification. Existing classification algorithms mainly focus on unintentional interference, such as co-channel interference and noise, with limited research on the problem of malicious interference in Multiple Input Multiple Output (MIMO) signal classification. This study proposes an intelligent MIMO signal classification algorithm based on fractional graph feature fusion. Initially, a high-order cumulant tensor model is constructed and regularized tensor decomposition is applied to reconstruct the MIMO signals. Subsequently, a feature extraction model using a fractional wavelet scattering network is designed to effectively capture the distinguishing features of signal constellations. Finally, a collaborative representation classifier based on the Grassmann manifold is utilized to amplify the differences between modulation categories, thereby improving classification performance. Simulation results indicate that the proposed algorithm effectively suppresses common communication interference and successfully classifies MIMO signals. Compared to existing methods, the proposed approach demonstrates significant performance improvements without requiring prior knowledge, such as noise power or channel coefficients.

This article treats the problems of the stability, boundedness, and stabilizing control of discrete-time semi-Markov jump systems (SMJSs) with fragmentary semi-Markov kernel (SMK) under persistent disturbances. Since the statistical characteristics of stochastic processes are difficult to describe precisely and comprehensively, the available SMK information may be fragmentary, and only a portion of the information is known. Regarding this problem, we propose new approaches that leverage all the known SMK information and derive new criteria for analysis and control. The feasibility therein can be enhanced compared to the existing approaches with inadequate utilization of the known SMK information. Additionally, a polytopic approach is proposed to approximate the unknown portion of the SMK information to enrich the information available for subsequent analysis and control design. This is achieved through constructing a polytopic quadratic Lyapunov-like function (LF), which further improves the feasibility. In this way, both the available information and the approximated unknown part about the SMK are incorporated. Meanwhile, the ultimate boundedness of the closed-loop semi-Markov jump linear system (SMJLS) is ensured in the mean-square sense without requiring the deviation between the state and its nominal one to converge at all times. We illustrate the validity and superiority of the proposed approach through a numerical example and a simulated chemical process example using a machine learning-based surrogate model.

This article is concerned with the output feedback security tracking control of a class of disturbed second-order nonlinear systems against denial-of-service (DoS) attacks. Novel radial basis function neural network (RBFNN)-based finite-time state observers are developed to estimate the system’s unavailable states. Adaptive filters are proposed to suppress the influences of disturbances and RBFNN approximation errors. Then, an RBFNN-based security controller is designed to alleviate the effects of nonlinear dynamics and DoS attacks based on the signals of observers and filters. It is established that the uniformly ultimately bounded output tracking results of the system can be obtained by utilizing an RBFNN-based finite-time observation and filtering compensation control designs through Lyapunov stability analysis. Comparative simulations are employed to display the feasibility and superiority of the designed RBFNN-based observation and filtering compensation control schemes of a nonlinear autonomous marine system (AMS).

This article investigates the security of a class of nonlinear systems subject to denial-of-service (DoS) attacks from an adversarial, data-aware perspective. An important-data-based (IDB) DoS attack strategy, inspired by event-triggered logic from the perspective of attackers, is skillfully constructed to increase the destruction of the attack. Unlike most existing DoS attack models that indiscriminately disrupt packet transmissions without considering packet content, the proposed IDB DOS attack strategy can identify the importance degree of the packets and focus on attacking the most important ones. The other goal of this article is to construct an$H_{\infty }$state estimator against the IDB DoS attack, which can achieve a particular system performance in the presence of attacks. By using the Lyapunov functional method, a sufficient condition is successfully obtained to ensure the asymptotical stability of the augmented system and the$H_{\infty }$interference suppression performance. Simulation examples indicate that: 1) the constructed IDB DoS attack strategy will result in a worse system degradation and 2) the designed$H_{\infty }$state estimator can effectively mitigate the impact of the potential IDB DoS attack.

This study finds and proves that strictly periodic scheduling is optimal in terms of energy efficiency within the context of remote state estimation. We model a problem where the sensor transmits local state estimates over an independent and identically distributed packet dropping channel to a remote estimator. From the angle of energy efficiency, we discover and explain that periodic scheduling can arbitrarily approach optimal scheduling in infinite horizon. Building upon this, we have derived explicit conclusions regarding the optimal periodic scheduling, underscoring that: 1) Under the condition of maximum energy efficiency, the optimal scheduling policy is to enforce a strict periodic scheduling; 2) A concrete expression for the identified strict scheduling period based on the system parameters has been established. The study culminates with a series of numerical simulations that showcase the efficacy of our theoretical findings.

In this paper, the stabilization problem is addressed for a class of globally Lipschitz nonlinear dynamical systems. An event-triggered impulsive control (ETIC) method is introduced to establish asymptotic stability and exponential stability criteria for the resulting closed-loop systems without and with time delay, respectively. Compared with the extant results, a new type of switching and event-triggered impulsive systems are considered, and some general asymptotic stability and exponential stability conditions are provided. It is proved that Zeno behaviors can be excluded and the control frequency can be appropriately regulated through the appropriate selection of event parameters. Finally, an application example of the Chua’s circuit is given to verify the efficiency of the approach and theoretical analyses. Note to Practitioners—Since impulsive control provides an easier and cheaper strategy than continuous control, it has been widely applied to many practical situations, such as control of satellite rendezvous, synchronization of permanent magnet synchronous motors, consensus of multi-agent systems and so on. Different from the time-triggered impulsive control methods, the proposed ETIC method can flexibly regulate the control frequency, which meets the increasing requirement for reducing unnecessary waste of communication resources in modern industries. Compared with the extant results, an ETIC method is established with time-varying threshold parameters and event interval parameters in this paper. Moreover, the effects of event parameters and time delay on convergence rate are analyzed, which can provide reference for improving system performance in practical applications.

Due to coupled nonlinearities and complex measurement noise, assess the condition of the rotor system remains a challenge, particularly in cases where historical run-to-failure data is lacking. To this end, we proposed a hierarchical physics-informed neural network (HPINN) to identify/discover the ordinary differential equations (ODEs) of a healthy/faulty rotor system from noise measurements and then assess the rotor condition based on the discovered ODEs. Specifically, the ODEs of a healthy rotor system are first stably identified from noisy measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODEs, the extra fault terms in the ODEs of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN, in which the phase compensation and alternating training strategy are developed to guarantee training convergence. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified with simulation and test bench datasets, showing the potential for practical industrial applications.Note to Practitioners—This paper investigates the health assessment problem (condition monitoring and fault diagnosis) of the rotor system, a critical component in large rotating machinery. The proposed HPINN provides a hierarchical framework to firstly identify the ODEs of healthy rotor system and then discover the ODEs of faulty rotor system with limited monitoring data (3-5 seconds data collected from sensor commonly, depending on the rotating speeds). With the mathematical terms of discovered fault, the fault can be diagnosed and a health indicator (HI) can be constructed to assess the condition of rotor system in a fully interpretative way. This approach is applicable to large rotating machinery in safety-critical industries, such as circulating water pumps.

This paper originates from two well-accepted challenges in the control of a quadrotor with a cable-suspended payload: 1) designing a refined controller based on high-precision payload swing modeling to achieve the quantized prescribed robustness; and 2) resolving the trajectory tracking performance degradation issue caused by input saturation. To combat these challenges, we start with establishing a precise payload swing model. The experimental investigation reveals the fact that neglect of realistic factors like cable-joint dry friction and cable elasticity has significant impacts on the precision of the existing models, especially under small swing angles. Therefore, we propose a new integrated drag model including a novel sign of the payload airspeed-dependent term, which lumps all payload swing damping factors together. This model is experimentally verified to be precise to provide the payload swing disturbance spectrum for quantitatively designing the bandwidth of the uncertainty and disturbance estimator (UDE). Furthermore, we resolve the input saturation issue by augmenting the classic UDE-based controller with a tracking differentiator (TD). Rigorous performance analysis derives a clear relationship between the control performance and the UDE parameter, which forms a simple yet effective parameter tuning guideline for practical applications to ensure the prescribed robustness and trajectory tracking accuracy. The effectiveness and advantage of the proposed controller are verified via comparative experiments in different flight scenarios. Note to Practitioners—The control input saturation issue and multi-parameter optimization are frequently encountered in engineering practices. When applying the TD to address the input saturation, the selection of parameter r in the TD depends on the reference continuity. Specifically, if the reference is continuous, then r can be large; otherwise r should be small to smooth the reference to avoid input saturation. For the multi-parameter optimization, the feedback gains$k_{p}$and$k_{d}$should be tuned first to guarantee the system stability, followed by decreasing the UDE parameter T to improve the system robustness. However, the feasible range of T may be restricted by the measurement noise and actuator bandwidth in practice. The proposed algorithm is applicable not only in aerial transportation systems but also in addressing other disturbance rejection problems.

This paper is concerned with the problem of robustly estimating the speed and acceleration of servo drives without the system model parameter information, depending on only the order of the open-loop system. The proposed filtering solution incorporates the disturbance observer (DOB) into the second-order pole-zero cancellation (PZC) observer. The main contributions fall into three parts. First, the model-free Luenberger observer as the first subsystem specifies its gain structure in the nonlinear form to invoke the second-order PZC. Second, the first-order nonlinear DOB as the second subsystem estimates the high-frequency disturbance to yield the compensation term for the second-order PZC observer. Third, the combination of these two subsystems eventually results in the diagonalized estimation error dynamics, which makes the performance adjusting process more convenient for field engineers. The experimental study confirms the effectiveness of the proposed filtering solution using a 500-W brushless DC (direct current) motor-based servo drive under various operation modes.

This paper delves into the distributed consensus control problem of nonlinear multiagent systems under the influence of actuator deception attacks based on a fixed directed topology. Diverging from the existing research, we develop a new actuator deception attack model, where attack signals are generated by an unmodeled system satisfying the input-to-state stable condition, and the unmodeled system utilizes the output consensus error of the agents and its delayed error information as the system input. In this condition, we put forward a novel distributed output feedback consensus control approach. First, we design the distributed controller with a compensator for the follower by the use of the relevant outputs of the agents, which is independent of the time delay and the states of the unmodeled system. Then, by constructing a new Lyapunov function with an adjustable power parameter, we can regulate the range of the functions describing the false data injected into the actuator. Additionally, through the combination of the exchange supply function method, we establish a strict proof that all agents can achieve exponential leader-following full-state consensus driven by the given controller. Finally, a simulation example is presented to demonstrate the effectiveness of the developed approach.

This paper investigates the problem of fixed-time stability of nonlinear systems subject to destabilizing delayed impulses. The existing literature usually uses inequality-based techniques to deal with impulses and delays, which leads to considerable conservatism, especially when systems contain large impulsive gains or large delays. In contrast, inspired by the Möbius transformation, this article presents necessary and sufficient criteria for fixed-time stability of nonlinear systems subject to periodic destabilizing impulses with delay, or even with arbitrarily finite delays. To conquer the challenge caused by large delays, this article proposes a generic framework that equivalently transforms a system with large delayed impulses into a series of systems with small delayed impulses. The equivalence of this transformation does not induce any conservatism. The criteria reveal that as the delay increases, the system switches between stability and instability. In addition, it proved that the impulsive systems under some conditions exhibit a subcritical Hopf bifurcation: If the impulsive frequency is less than a critical value, then the system converges globally to an equilibrium point in a fixed time; otherwise the system oscillates periodically. Finally, examples of circuit systems are used to validate the theoretical results.

Tackling the challenge of open-switch (OS) fault diagnostics in grid-tied three-level neutral point clamped (NPC) inverters with parameter uncertainty, this paper introduces a fault diagnosis method that integrates a variable forgetting factor bias-compensation recursive least squares (VFFBCRLS) algorithm with a novel discrete disturbance sliding mode observer (DSMO) for three-level inverters. The proposed approach initially employs a VFFBCRlS algorithm to obtain the uncertain parameters of the inverter. Building upon this foundation, a novel discrete DSMO is introduced to obtain the output currents rapidly and accurately. Then, an adaptive fault detection variable is constructed based on the norm of the residual between the measured and the estimated currents, ensuring the accuracy and robustness of the detection algorithm. Finally, a precise identification of OS faults in grid-tied inverters is achieved through the establishment of a localization mechanism. The hardware-in-the-loop (HIL) test results provide validation for the efficacy and robustness of the proposed method.

This article investigates the issue of observer-based fixed-time-synchronized tracking control for Euler-Lagrange (EL) systems with uncertain dynamics, bias-actuator faults and external disturbances. A novel fixed-time observer is proposed to reconstruct the actuator faults and system uncertainties, so that the observation error can reduce to zero within a fixed time. A fixed-time stable system with fast convergence rate is developed by using switching terms to design a newly sliding mode variable with the norm-normalized sign function. Then, on the basis of the reconstructed information from the fixed-time observer and the sliding mode variable, a robust control law is developed to realize fixed-time-synchronized stability of the EL system. It is concluded by Lyapunov stability theorem that the proposed method not only can guarantee that the boundary of convergence time is irrelevant of initial values of the system states, but also make all elements of the system tracking errors reach the origin simultaneously under the influence of actuator faults, external disturbances and uncertain dynamics. Finally, several comparative simulations are carried out to validate the developed observation and control schemes as well as their effectiveness.

Autonomous object-searching is crucial for various applications of unmanned aerial vehicles (UAVs). Considering the fact that existing autonomous exploration methods either focus only on maximizing the exploration of unknown areas or suffer from insufficient searches due to repeated and unnecessary exploration, this article introduces an effective object-searching strategy for UAVs in large-scale and complex environments. A novel method is proposed to empower UAVs with the capability to conduct fast, secure, and efficient searches for interested objects in large-scale and complex environments. A Kalman filter-based YOLO algorithm is first proposed to achieve robust object position estimation in cluttered and occlusion-prone scenarios, and a mode-based method is then introduced to conduct a computationally efficient viewpoint generation. A hierarchical searching method is proposed, which not only can increase computational and search efficiency but also can leverage frontier data for search-planning, including coarse global searching paths and optimizing local refined searching trajectories. Experimental results in six different environments indicate that our proposed method outperforms existing techniques in terms of both reduced searching times and computing time. Moreover, the effectiveness of the proposed method is substantiated in various real-world scenarios.

The principal innovative contribution of this study resides in the introduction of a category of fractional delayed large-scale neural networks characterized by intricate topological structures. Additionally, this article provides a comprehensive exploration of novel outcomes linked to fractional order-induced bifurcations in large-scale networks. In the initial step, the correlation of the artificial neural network and the graphical neural network is established through the Mason's diagram method. Subsequently, the system's characteristic equations are derived by employing the Coates' flow graph decomposition method. Moving on, through the concept of the global element, an exhaustive investigation delves into the distribution of eigenroots. The sum of synaptic transmission delays among neurons is considered as a bifurcation parameter, with an analysis focused on the stability of the trivial equilibrium and the existence of the Hopf bifurcation. Following this, the optimal fractional order-dependent stability interval is determined using the implicit function array curve method, presenting a novel approach for critical value determination. Finally, the drawn conclusions are substantiated through multiple sets of computer simulations. It is indicated that an increase in delay precipitates the onset of Hopf bifurcation. Moreover, a reduction in the fractional order significantly improves the steady-state performance of the system. However, once the fractional order value descends below the left stability boundary, the system's stability is compromised, leading to the emergence of periodic oscillations. The prediction algorithm proposed in this article offers valuable insights into selecting the appropriate fractional order for large-scale complex networks.

This article investigates the state estimation and security control problem for discrete-time dual-rate cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. The asynchrony predicament between different signals of dual-rate CPSs, exacerbated by the impact of cyber attacks on the sensor-to-controller channel, substantially increases the complexity of state estimation and control processes. Based on the signal-to-interference-plus-noise ratio and two-channel probability descriptions, an improved sample-and-hold (SAH) estimator is applied to dual-rate CPSs, ensuring favorable state estimates while enduring low-frequency sampling and DoS attacks. Furthermore, to solve the performance degradation problem posed by the SAH algorithm, an alternating-sampling-prediction (ASP)-based estimation method is proposed. At each fast-update moment, the predictor generates virtual outputs. The estimator can reconstruct complete state information by alternately using incomplete sampling data and iterative predictive information. Compared with the SAH method, the proposed ASP-based approach significantly enhances the control performance of dual-rate CPSs. Building on two valid estimation methods, the corresponding security control inputs are designed, guaranteeing both ideal control performance and resilience against attacks. Using convex optimization analysis, both estimator and controller gains are calculated to realize the stochastic stability of closed-loop dual-rate CPSs. Finally, the effectiveness and intercomparisons of the two estimation methods are shown by simulating a satellite yaw-angle control system and a quadrotor landing control experiment.

This article introduces an innovative approach to simultaneously estimate time-domain and spatiotemporal faults in nonlinear distributed parameter systems (NDPSs)nonlinear distributed parameter systems (NDPSs) under external disturbances. First, the establishment of an iterative learning observer that accounts for both temporal and spatial changes is presented. Next, a fault estimation law is devised utilizing a distinct full iterative learning (FIL)full iterative learning (FIL) technique, facilitating rapid and precise estimation of fault signals while mitigating the impact of external disturbances. Furthermore, the adoption of the $\lambda $ -norm method aids in simplifying the determination of convergence conditions and gain matrix calculations. Lastly, comprehensive simulation results validate the efficacy of the developed approach, underscoring its adeptness in efficiently and precisely estimating faults across both time and spatiotemporal domains.

The multistability and its application in associative memories are investigated in this article for state-dependent switched fractional-order Hopfield neural networks (FOHNNs) with Mexican-hat activation function (AF). Based on the Brouwer's fixed point theorem, the contraction mapping principle and the theory of fractional-order differential equations, some sufficient conditions are established to ensure the existence, exact existence and local stability of multiple equilibrium points (EPs) in the sense of Filippov, in which the positively invariant sets are also estimated. In particular, the analysis concerning the existence and stability of EPs is quite different from those in the literature because the considered system involves both fractional-order derivative and state-dependent switching. It should be pointed out that, compared with the results in the literature, the total number of EPs and stable EPs increases from and to and , respectively, where with being the system dimension. Besides, a new method is designed to realize associative memories for grayscale and color images by introducing a deviation vector, which, in comparison with the existing works, not only improves the utilization efficiency of EPs, but also reduces the system dimension and computational burden. Finally, the effectiveness of the theoretical results is illustrated by four numerical simulations.

In this article, we propose a new concept called average impulsive delay-gain (AIDG) for studying the synchronization of coupled neural networks (CNNs). Based on the viewpoints of impulsive control and impulsive perturbation, we establish some globally exponential synchronization criteria for CNNs. Our methods are well-suited for addressing the synchronization problems of systems subject to hybrid delayed impulses with time-varying impulsive delay and gain. Moreover, we prove that the AIDG has both positive and negative effects on synchronization. Compared to existing research, our conclusions are more applicable and less conservative as the considered hybrid delayed impulses involve more flexible cases. Finally, we validate the effectiveness of our proposed results by applying them to small-world and scale-free network models.

Spatiotemporal dynamics in the brain have been recognized as strongly related to the formation of perceived and cognitive diseases, such as delusions and hallucinations in Alzheimer's disease. However, two practical considerations are rarely mentioned in related mechanism research: the connectomics networking and the anomalous diffusion generated by the complex medium between neurons and the complex topology of neural networks, respectively. Furthermore, how to optimize the corresponding dynamics behaviors has excellent implications for treating brain diseases. This article first realizes the networking under connectomics for an anomalous-diffusion single-neuron model and applies a nonlinear state feedback control to generate optimized dynamic behaviors, which provides a paradigm of nonequilibrium self-organization driven by anomalous diffusion. Then, by tracing the root distribution of the characteristic equation, some controlled conditions causing or inhibiting Turing instability and Hopf bifurcation are deduced, and the effects of self-diffusion and cross diffusion on Turing instability range are also revealed. At last, thorough numerical simulations are updated to illustrate the results. It is emphasized that delay, self-diffusion, cross diffusion, and fractional order occupy dominant positions in determining the network's spatiotemporal dynamics, and utilizing the control strategy can efficiently reduce Turing instability and delay Hopf bifurcation.

In this article, a distributed fault estimation (DFE) approach for switched interconnected nonlinear systems (SINSs) with time delays and external disturbances is proposed using a novel segmented iterative learning scheme (SILS). First, through the utilization of interrelated information among subsystems, a distributed iterative learning observer is developed to enhance the accuracy of fault estimation results, which can realize the fault estimation of all subsystems under time delays and external disturbances. Simultaneously, to facilitate rapid fault information tracking and significantly reduce sensitivity to interference, a new SILS-based fault estimation law is constructed by combining the idea of segmented design with the method of variable gain. Then, an assessment of the convergence of the established fault estimation methodology is conducted, and the configurations of observer gain matrices and iterative learning gain matrices are duly accomplished. Finally, simulation results are showcased to demonstrate the superiority and feasibility of the developed fault estimation approach.

In this article, the coordination control problem of discrete-time multiagent systems (MASs) affected by uncertainties, namely unknown initial states and external disturbances, is considered. Inspired by the interval observer constructed by the single system, the definition of distributed interval observer for discrete-time MASs is given, in which the control protocol of each agent obtained by solving a modified algebraic Riccati equation depends on the bounded information of the interval observer connected to itself and its neighbors. By the cooperativity theory and Lyapunov stability theory, it is established that the distributed interval observer can not only access some information about MASs at any instant, that is, the upper and lower bounds of each component of the agent state, but also realize the cooperative behavior of MASs under some essential conditions involving network synchronization and the unstable eigenvalue of the system matrix. In addition, with the help of a new time-varying transformation matrix, the new interval observer is constructed to eliminate the non-negative constraint. Finally, two numerical simulations are provided to confirm the validity of the derived results.

This work explores the issue of multistability for a competitive neural network (NN) class with sigmoidal activation functions (AFs) involving state-dependent switching and fractional-order derivative. Specifically, first, we consider three different switching point locations, and establish some sufficient criteria ensuring that NNs with$n$-neurons can have, and only have,$5^{n_{1}}\cdot 3^{n_{2}}$equilibrium points (EPs) with$n_{1}+n_{2}=n$, by utilizing the geometric features of the sigmoidal functions, the fixed point theorem, the Filippov’s EP definition, and the contraction mapping theorem. Then, based on novel Lyapunov functions and by applying the fractional-order calculus theory, it is demonstrated that$3^{n_{1}}\cdot 2^{n_{2}}$out of$5^{n_{1}}\cdot 3^{n_{2}}$total EPs are locally stable. This work’s investigation reveals that competitive NNs with switching afford more storage capacity compared to the nonswitching case. Additionally, our results are valid for the integer-order and fractional-order switched NNs, improving and generalizing current works. Furthermore, two numerical examples and an application example of associative memory are provided to validate the effectiveness of the theoretical findings, and the way various fractional orders affect the NNs’ convergence speed is shown through simulations.

This article is committed to investigate thepth moment input-to-state stability for stochastic nonlinear delayed systems involving multiple impulses. Via a series of auxiliary equations, Razumikhin method and stochastic analysis approach, we derive some new stability results under the combination of impulsive control and impulsive disturbance. For all we know, this article is the first attempt to study such a stability issue for the suggested systems. Additionally, the concepts of stabilizing impulse average dwell time (SIADT) and destabilizing impulse average dwell time (DIADT) are proposed, respectively, owing to the presence of multiple impulses. It should also be pointed out that the obtained criteria can loose some restrictions of the existing results and have wider applications. Particularly, we allow the cumulative strength of multiple impulses over a period to be greater than 1, which is more applicable to some extent compared with most of the existing literatures. Finally, the effectiveness and validity of the theory are supported by two examples.

Existing fully distributed protocols for flocking are built upon the network of mobile agents with only cooperative interactions. Rather than investigating such networks, this paper deals with the problem of leader-follower flocking control over signed communication networks, where there impose less restrictions on the distributions of cooperations and competitions in the network of mobile agents. Firstly, for the second-order dynamics model, a novel state feedback controller that relies only on the relative velocity information of neighboring agents is developed. Secondly, the solvability of flocking control problem is transformed to the asymptotic stability of error system, and the latter is guaranteed by treating the product convergence of infinite super-stochastic matrices. Then, sufficient condition for the solvability of flocking control problem is proposed by establishing the inequality constraints on positive and negative edge weights. Finally, a numerical example is performed to illustrate the correctness of the theoretical result.

The expansion of data coverage and the accuracy of decoding of the narrowband-internet of things (NB-IOT) mainly depend on the quality of channel equalizers. Without using training sequences, blind equalization is an effective method to overcome adverse effects in the internet of things (IoT). The constant modulus algorithm (CMA) has become a favorite blind equalization algorithm due to its least mean square (LMS)-like complexity and desirable robustness property. However, the transmission of high-order quadrature amplitude modulation (QAM) signals in the IoT can degrade its performance and the convergence speed. This paper investigates a family of modified constant modulus algorithms for blind equalization of IoT using high-order QAM. Our theoretical analysis for the first time illustrates that the classical CMA has the problem of artificial error using high-order QAM signals. In order to effectively deal with these issues, a modified constant modulus algorithm (MCMA) is proposed to decrease the modulus matched error, which can efficiently suppress the artificial error and misadjustment at the expense of reduced sample usage rate. Moreover, a generalized form of the MCMA (GMCMA) is developed to improve the sample usage rate and guarantee the desirable equalization performance. Two modified Newton methods (MNMs) for the proposed MCMA and GMCMA are constructed to obtain the optimal equalizer. Theoretical proofs are presented to show the fast convergence speed of the two MNMs. Numerical results show that our methods outperform other methods in terms of equalization performance and convergence speed.

For the correlated input, the Volterra kernel-based least mean-square (LMS) algorithm in the time-domain exhibits a slow learning rate caused by the large eigenvalue spread of the input covariance matrix. To tackle such an issue, this paper develops a novel frequency-domain Volterra kernel-based filter, resulting in the periodic update constrained frequency-domain second-order Volterra normalized LMS (named as P-CFDSOV-NLMS1) algorithm. Subsequently, by using one- and two-dimensional discrete Fourier transforms (DFTs) simultaneously, another frequency-domain implementation and corresponding P-CFDSOV-NLMS2 algorithm are constructed. In contrast, the P-CFDSOV-NLMS1 scheme only requires one-dimensional DFT operations and takes advantage of the joint information between the block input vectors. Then, the mean and mean-square convergence behaviors of the P-CFDSOV-NLMS1 algorithm are investigated. Furthermore, the designed frequency-domain method is extended to three different widely complex-valued Volterra kernel-based models. Finally, computer simulations reveal that the suggested algorithms outperform the previously reported frequency-domain techniques in terms of convergence speed and tracking ability.

In this article, a new adaptive second-order sliding mode (SOSM) controller is designed for a type of nonlinear systems with parametric uncertainties and time-varying asymmetric output constraints. There are two notable features in the obtained results. One feature is that a two-layer adaptive mechanism is established to reconstruct the upper bound of the unknown uncertainty, where the uncertainty is bounded by an unknown state-dependent structure rather than an unknown constant. The other is that a universal tangent-type barrier Lyapunov function (Tan-BLF) is constructed to address the time-varying asymmetric output constraint requirements. By combining the designed Tan-BLF, adaptive control and revamped adding a power integrator techniques together, a novel design procedure is introduced to systematically construct an adaptive SOSM controller. A rigorous Lyapunov analysis indicates that under the developed control framework, the finite-time stability of the whole system and the realization of the prescribed constraints can be guaranteed. Finally, two simulation cases containing a practical one are provided to demonstrate the effectiveness of the proposed control scheme.Note to Practitioners—This article is motivated by the desire to deal with nonlinear systems in the presence of parameter uncertainties and time-varying output constraints. In practical applications, on the one hand, parameter uncertainties are an unavoidable problem for real-world systems, and on the other hand, output constraints are widely present in many engineering systems due to safety considerations and inherent physical constraints. Until now, these issues have not been solved effectively. Therefore, to resolve these issues, a new adaptive SOSM controller is constructed in this article by using the adaptive control technique, adding a power integrator method, time-varying asymmetric Tan-BLF and Lyapunov finite-time stability theory. The proposed control strategy not only ensures the finite-time stability of the resulting closed-loop system, but also guarantees that the system output satisfies the preset time-varying output constraints. In the future, we will attempt to apply the proposed method to more practical systems.

Turing patterns in diffusion neural networks are strongly associated with the performance of artificial intelligence model. However, two practical considerations are rarely mentioned in the mechanism research on the problem of diffusion-coupled cellular neural networks (CNNs): the effects of memristor and three-dimensional (3-D) network structure. Furthermore, facilitating the formation of the determined Turing pattern is expected to make the neural network exhibit the desired intelligence. This paper first realizes the 3-D diffusive networking for a primary cell circuit with memristor characteristics and utilizes a proportional-derivative (PD) control strategy to drive the pattern formation. Next, the characteristic equation is derived using the spatial eigenfunction-based decoupling method. Then, by tracing the root distribution of the characteristic equation, some analytical conditions for the stability or forming Turing patterns are deduced. In addition, the central manifold reduction and linear analysis approaches are utilized to derive the amplitude equations of 3-D Turing patterns and investigate their stability. At last, some numerical simulations are provided to illustrate the results. It is also demonstrated that PD control and memristor occupy dominant positions in various CNNs’ Turing patterns. After determining the pattern stability, the implementation of PD control can be considered an effective means to facilitate stable networks to experience Turing instability and generate switchable 3-D pattern.

Accurate fault detection and localization are essential for single-phase three-level (SPTL) rectifier systems with high reliability requirements. However, power switch faults, grid-side current sensor (CS) faults, and DC-link voltage sensor (VS) faults can all contribute to distorted output in the rectifier system, posing challenges for existing diagnostic methods tailored for single-type faults, as they struggle to distinguish between these various faults. Therefore, this study proposes a comprehensive diagnosis technology for open-circuit (OC) faults, CS faults, and VS faults of SPTL rectifiers on the basis of a reduced-order observer. To achieve this, the method begins by expanding and transforming the state equation of the rectifier with faults, ensuring complete decoupling of the OC fault vector from the initial system states and sensor faults. Subsequently, an assessment of the initial system state, CS faults, and VS faults is achieved via the design of a reduced-order observer. Using these estimation results, fault detection variable and its adaptive thresholds is designed, along with fault-distinguishing variables to differentiate between sensor faults and OC faults. Simultaneously, sensor fault identification method and OC fault location method are introduced. Finally, the validity and resilience of the comprehensive diagnostic approach are confirmed through hardware-in-the-loop (HIL) test results under diverse scenarios.

In this paper, a novel fixed-time composite anti-disturbance control framework is proposed for$n$-degrees of freedom ($n$-DOF) flexible-link manipulator systems with modelling uncertainties and external disturbances. The aim is to ensure that the considered system achieves suppression of elastic vibrations while tracking time-varying trajectories. First, based on the singular perturbation theory, the nonlinear coupled system is decomposed into a slow subsystem and a fast subsystem. Second, a disturbance observer based on the super-twisting algorithm is designed to estimate multiple disturbances within a fixed time, and then the fixed-time tracking control scheme for the slow subsystem is developed by means of the designed observer. For fast dynamics, a barrier Lyapunov function is introduced to implement the fixed-time vibration suppression control scheme. The fixed-time stability of the tracking error system is demonstrated via the Lyapunov function method. Finally, simulation results of two-link flexible manipulator systems verify that the proposed control algorithm can improve the tracking speed and precision.

In leader-follower multiagent systems (MASs), seeking an efficient scheme to select a set of agents as leaders is important for realizing the expected cooperative performance. In this article, the problem of minimal leader selection is investigated for impulsive general linear MASs with switching topologies. This study focuses on selecting a set of agents as leaders that receive information from a reference signal directly, while minimizing the number of leaders, subject to consensus tracking performance. First, adopting the average dwell time technique and a time-ratio constraint, an explicit criterion for consensus tracking is derived as prepreparation for leader selection. Second, applying the submodular optimization framework, leader selection metrics are established based on the derived criterion. Third, employing the greedy rule, an efficient leader selection scheme is presented according to the established metrics. The scheme comprises two polynomial-time algorithms that return selected leader sets within a logarithmic bound of the optimum. Finally, the effectiveness of the developed leader selection scheme is verified using an illustrative example.

This article focuses on the reachable set synthesis problem for singular Takagi-Sugeno fuzzy systems with time-varying delay. The main contribution is that we design a proportional plus derivative state feedback controller to ensure that the singular fuzzy system is normal and the system states are bounded by a derived ellipsoid. In the light of the Lyapunov stability theory and the parallel distributed compensation method, the sufficient criteria are shown in the format of linear matrix inequalities. Furthermore, we investigate another case of reachable set synthesis, where the reachable set to be found is contained in a given ellipsoid. Finally, we use two examples to exhibit the usefulness of the proposed method.

This work examines the distributed leader-following consensus problem of feedforward nonlinear delayed multiagent systems involving directed switching topologies. In contrast to the existing studies, we focus on time delays acting on the outputs of feedforward nonlinear systems, and we permit that the partial topology dissatisfy the directed spanning tree condition. In the cases, we present a novel output feedback-based general switched cascade compensation control method that addresses the above-mentioned problem. First, we put forward a distributed switched cascade compensator by introducing multiple equations, and we design the delay-dependent distributed output feedback controller with the compensator. Subsequently, when the control parameters-dependent linear matrix inequality is met and the switching signal of the topologies obeys a general switching law, we prove that the established controller can render that the follower's state asymptotically tracks the leader's state by employing an appropriate Lyapunov-Krasovskii functional. The given algorithm allows output delays to be arbitrarily large and increases the switching frequency of the topologies. A numerical simulation is presented to demonstrate the practicability of our proposed strategy.

Event-triggered (ET) consensus of linear multiagent systems with relative output sensing on undirected graphs is studied. Two output-feedback protocols with static and time-varying coupling strengths, respectively, are proposed, which, different from the existing results in relative output sensing, integrate effective ET strategies to reduce the communication burdens between agents. To ensure the closed-loop consensus, design conditions about the gain matrices, coupling strengths, and event-triggering functions are derived. Zeno behaviors are also shown to be excluded from the triggering process. In addition, recursive algorithms are devised for computing the continuous-time relative signals required by the event-triggering functions, so that continuous monitoring of neighbors is circumvented. Numerical examples finally demonstrate the effectiveness of the proposed design method.

The robust LQ optimal regulator problem for discrete-time uncertain singular Markov jump systems (SMJSs) is solved by introducing a new quadratic cost function established by the penalty function method, which combines the penalty function and the weighting matrices. First, the indefinite robust optimal regulator problem for uncertain SMJSs is transformed into the robust optimal regulator problem with positive definite weighting matrices for uncertain Markov jump systems (MJSs). The transformed robust LQ problem is settled by the robust least-squares method, and the condition of the existence and analytic form of the robust optimal regulator are proposed. On the infinite horizon, the optimal state feedback is obtained, which can guarantee the regularity, causality, and stochastic stability of the corresponding optimal closed-loop system and eliminate the uncertain parameters of the closed-loop system. A numerical example and a practical example of DC motor are used to verify the validity of the conclusions.

This article is concerned with the security problems for networked Takagi–Sugeno (T–S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different from most existing DoS attack models, the proposed attack mechanism can utilize the information of packets, evaluate the importance degree of packets, and only attack the most “important” ones. As such, a larger system performance degradation can be expected. Second, corresponding to the proposed IDB DoS mechanism, a resilient$H_{\infty }$fuzzy filter is designed from the defender’s point of view to alleviate the negative effect of the attack. Furthermore, since the defender does not know the attack parameter, an algorithm is designed to estimate it. In a word, a unified attack-defense framework is developed in this article for networked T–S fuzzy systems with asynchronous premise constraints. With the help of the Lyapunov functional method, sufficient conditions are successfully established to compute the desired filtering gains and ensure the$H_{\infty }$performance of the filtering error system. Finally, two examples are exploited to demonstrate the destructiveness of the proposed IDB DoS attack and the usefulness of the developed resilient$H_{\infty }$filter.

This article devises a new dynamic double event-triggered anti-disturbance tracking control scheme for a 2-degree of freedom (DOF) laboratory helicopter subject to external time-varying disturbances and load fluctuation by using the generalized proportional-integral observer technique. The helicopter system is separated into two subsystems in the proposed control method, i.e., the pitch subsystem and the yaw subsystem. Each subsystem includes a discrete-time dynamic double event-triggering mechanism (DDETM), and the control laws of the two subsystems are independent of each other. There are two triggering conditions in the designed double triggering mechanism: one is designed based on the system states and the other is based on the lumped disturbance estimation. These two triggering conditions form a competitive relationship such that the controller updates the control signal as long as one of the triggering conditions is satisfied. Theoretical analysis is provided for achieving the better communication and control performance of the proposed DDETM-based robust control method. Through rigorous stability analysis, it is proved that the closed-loop hybrid system is globally ultimately bounded. At last, numerical simulations show that the suggested control strategy not only reduces the event-triggering number, but also improves the initial dynamic performance of the system.

This paper focuses on the design of a privacy-preserving distributed Kalman filtering algorithm for a class of linear time-varying systems in signal-to-interference-plus-noise ratio (SINR)-based sensor networks, where packet dropouts may occur in information transmission between neighboring sensor nodes. Considering the potential occurrence of eavesdropping attacks during information transmission, which is common due to the inherent vulnerability of SINR-based sensor networks, a new class of distributed secure Kalman filtering algorithm has been developed. The presented algorithm incorporates a modified ElGamal cryptosystem and adaptive fusion weights to significantly enhance security, resist privacy leakage, and bolster robustness against packet dropping. Then, a detailed performance analysis for the presented distributed secure Kalman filtering algorithm is conducted, where the security and unbiasedness of the designed algorithm are discussed. Sufficient conditions for the stability of the estimation error are further established to ensure that the estimation error is ultimately bounded in the almost sure sense. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithm.

This article revisits the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs) in the presence of disturbance in the input channel. A new Lyapunov approach based on double Lyapunov functionals is introduced for analyzing exponential input-to-state stability (EISS) of discrete impulsive delayed systems. In the framework of double Lyapunov functionals, a pair of timer-dependent Lyapunov functionals are constructed for impulsive DDNNs. The pair of Lyapunov functionals can introduce more degrees of freedom that not only can be exploited to reduce the conservatism of the previous methods, but also make it possible to design variable gain impulsive controllers. New design criteria for impulsive stabilization and impulsive synchronization are derived in terms of linear matrix inequalities. Numerical results show that compared with the constant gain design technique, the proposed variable gain design technique can accept larger impulse intervals and equip the impulsive controllers with a stronger disturbance attenuation ability. Applications to digital signal encryption and image encryption are provided which validate the effectiveness of the theoretical results.

To improve the learning performance of the conventional diffusion least mean square (DLMS) algorithms, this article proposes Bayesian-learning-based DLMS (BL-DLMS) algorithms. First, the proposed BL-DLMS algorithms are inferred from a Gaussian state-space model-based Bayesian learning perspective. By performing Bayesian inference in the given Gaussian state-space model, a variable step-size and an estimation of the uncertainty of information of interest at each node are obtained for the proposed BL-DLMS algorithms. Next, a control method at each node is designed to improve the tracking performance of the proposed BL-DLMS algorithms in the sudden change scenario. Then, a lower bound on the variable step-size of each node of the proposed BL-DLMS algorithms is derived to maintain the optimal steady-state performance in the nonstationary scenario (unknown parameter vector of interest is time-varying). Afterward, the mean stability and the transient and steady-state mean square performance of the proposed BL-DLMS algorithms are analyzed in the nonstationary scenario. In addition, two Bayesian-learning-based diffusion bias-compensated LMS algorithms are proposed to handle the noisy inputs. Finally, the superior learning performance of the proposed learning algorithms is verified by numerical simulations, and the simulated results are in good agreement with the theoretical results.

In this article, a general Filippov system with nondifferentiable and unbounded delays is studied, which is different from the previous delayed Filippov systems. First, based on the differential inclusion theory and Kakutani’s fixed point theorem, the periodicity of solutions is proved and sufficient criteria are derived. Second, fixed-time (FxT) stabilization is studied by designing a new control law with a unified steepness exponent, which is more simpler than the previous ones with two steepness exponents. Since the delays are nondifferentiable and unbounded, rather than constructing the Lyapunov–Krasovskii functions, by constructing a suitable Lyapunov function, the FxT stabilization is analyzed and estimation of the settling-time (ST) is given through analyzing the state variables inside and outside the unit spherical area. The energy consumption is also estimated when the FxT stabilization is achieved under the designed controller. Third, given that smaller ST and lower energy consumption are usually preferred, the optimization problem is further considered. The optimal control parameters are selected based on the normalization approach and maximum principle. Finally, the validity of the theoretical results is demonstrated by a numerical example, where the delay function is composed of the Takagi function and the absolute value function.

As the simplest feature extraction, traditional hand-crafted transient-state features have been widely used in the area of electronic noses (e-noses). However, the influence of noise in the calculation of numerical differentiation leads to inaccuracy and instability in extracting these features. To tackle this issue, a novel numerical differentiation algorithm is proposed, which uses kernel-based regularization. The proposed method can provide accurate and stable transient-state features by directly estimating high-order derivatives from the noise-contaminated sensor’s reading. The feature representation is a prerequisite for the good performance of e-noses. Nevertheless, it should be noted that this performance in real applications can still be affected by other factors, such as sensor drift and the disturbance of nontarget odors. These issues can be addressed by applying a framework of domain adaptation and one-class classification. The proposed method and the adopted framework are verified in a field experiment, which identifies the odor of four targets and two disturbance whiskies measured by a self-designed e-nose system. The classification accuracy with traditional features is improved from$\mathbf{71.90\%}$to$\mathbf{86.36\%}$, showing the good potential of the proposed method for application in the area of e-noses.

This article considers a sort of continuous-time switched T–S fuzzy systems, in which each subsystem switches based on the mode-dependent average dwell time and transition probability and the mixed delay includes time-varying and infinite-time distributed delay. An event-triggered controller (ETC) with mode-dependent random deception attacks is put forward such that the considered system realizes exponential stabilization almost surely (ES a.s.). The ETC is not only mode-dependent but also excludes Zeno behavior automatically with tunable parameters to adjust the event-triggering (ET) numbers according to practical needs. By using the ergodic theory and designing Lyapunov–Krasovskii functional, two criteria are set up to ensure the ES a.s. It is interesting to discover that the ETC is not necessary to control each mode to be stable and the dwell time of an unstable mode can be very large, which greatly reduces the conservatism and saves the control cost. Moreover, the weights of ET mechanism and control gains are obtained for all the switching modes by solving linear matrix inequalities. A simulation example is given to illustrate the merits of theoretical analysis.

This article pays attention to finite-time (FnT)$p$th moment asymptotically bounded (MAB) for stochastic nonlinear systems (SNSs) and its application to$p$th moment quasi-synchronization (MQS) of stochastic neural networks (SNNs) based on parameter mismatches. First, this article develops FnT asymptotically bounded theorems SNSs. In detail, several new FnT$p$th MAB theorems of the SNSs are proposed, the mathematical expression of finite settlement time is obtained, and the bounds of MAB are estimated. Second, new sufficient conditions are designed to ensure FnT$p$th MQS of SNNs. In particular, novel FnT$p$th MQS conditions of the SNNs enlarge the value of$p$. Moreover, when the initial value of the system meets certain conditions, the smaller the$p$is, the smaller the system synchronization control energy consumption is, which can be more meaningful. Finally, a numeric example illustrates the validity of the methods.

Only when understanding hackers” tactics, can we thwart their attacks. With this spirit, this paper studies how hackers can effectively launch the so-called ‘targeted node attacks”, in which iterative attacks are staged on a network, and in each iteration the most important node is removed. In the existing attacks for weighted networks, the node importance is typically measured by the centralities related to shortest paths, and the attack effectiveness is also measured mostly by shortest-path-related metrics. However, this paper argues that flows can better reflect network functioning than shortest paths for those networks with carrying traffic as the main functionality. Thus, this paper proposes metrics based on flows for measuring the node importance and the attack effectiveness, respectively. Our node importance metrics include three flow-based centralities (flow betweenness, current-flow betweenness and current-flow closeness), which have not been proposed for use in the attacks on weighted networks yet. Our attack effectiveness metric is a new one proposed by us based on average network flow. Extensive experiments on both artificial and real-world networks show that the attack methods with our three suggested centralities are more effective than the existing attack methods when evaluated under our proposed attack effectiveness metric.

In multi-agent systems with leader-follower dynamics, choosing a subset of agents as leaders is a critical step in achieving the desired coordination performance. In this study, by considering consensus tracking for general linear multi-agent systems under switching topologies, we address the problem of selecting a minimum-size set of leaders by leveraging the submodularity ratio. First, using the dwell time technique, a criterion is derived to ensure that the states of all agents can converge to a reference trajectory that is directly tracked by each leader. Second, exploiting the derived consensus tracking criterion, the metrics with a structure of the Euclidean distance between specific vectors and the space spanned by an iteratively updated matrix are established to identify a set of leaders, and then the corresponding bound of the submodularity ratio is proposed. Third, combining the derived criterion and the constructed metrics, a leader selection scheme is presented together with three polynomial-time algorithms, and the related provable optimality bound of each algorithm can be obtained by leveraging the proposed bound of the submodularity ratio. Finally, illustrative examples are provided to verify the effectiveness of the proposed leader selection scheme.

This paper is concerned with the finite time$H_{\infty} $composite anti-disturbance control problem for Markov switched descriptor systems with multiple disturbances and packet loss via a disturbance observer. Significantly, the switching topology of descriptor systems is controlled by nonhomogeneous Markov switching processes, whose time-varying transition probability is limited by a convex hull. Subsequently, a Bernoulli random variable is exploited to characterize the intermittent measurement mode behavior of controller-actuator packet loss. Furthermore, the stochastic$H_{\infty} $finite time boundiness of the composite system and simultaneously suppression and rejection of external disturbances are established by resorting to disturbance observer-based robust control (DOBC) strategy and a new stochastic Lyapunov function technique. More importantly, a relaxed variable method is provided to eliminate the coupling between Lyapunov variables and the system matrix in the process of stability analysis, instead of eliminating the coupling by means of commonly-used traditional inequalities, which effectively increases the flexibility of the obtained stable results and greatly reduces the computational complexity of controller/observer in the existing works. Finally, the effectiveness and practicability of the developed results are verified by two practical engineering models.

This study concentrates on the differential private distributed optimization problem with an event-triggered mechanism, whose goals include preserving the privacy of agents’ initial states and local cost functions and improving communication efficiency. A distributed event-triggered mechanism is integrated into the differentially private subgradient-push distributed optimization algorithm and then a new algorithm named as DP-ETSP is designed, where the real-time information propagation among agents is avoided. Additionally, under the proposed event-triggered mechanism, an analysis of mean-square consensus and optimality over time-varying directed networks is made when the added Laplace noises meet some specific decaying conditions. Convergence rate results are further established under a specific stepsize, which are equal to the rate of stochastic gradient-push algorithm without event-triggered communication. Moreover, the differential privacy preservation performance is analyzed and the rule for selecting privacy level is discussed. Finally, the feasibility and effectiveness of DP-ETSP are verified in two simulation cases.

This paper studies the problem of leaderless CC (cluster consensus) for second-order MAS (multi-agent systems) with inherent nonlinearity under directed topology. Interactions among agents in each cluster are cooperative, but interactions between agents in different clusters can be cooperative or antagonistic. Also, there is a directed spanning tree in each subdigraph formed by interactions among agents within each cluster. Based on a reference model taking the relative error with respect to the original system states as an input, an adaptive CC protocol is designed under general inter-cluster couplings. A variable transformation is applied to transform the CC problem into a stability problem, and some sufficient conditions for CC are derived. Since these conditions are expressed as logarithmic norm inequalities of reduced-order matrices, they are easy to verify. Furthermore, when there are no nonlinear functions, a corollary of our main results is provided to substantially improve some existing results on the leaderless CC problem. Additionally, by introducing a fully distributed adaptive observer, a fully distributed adaptive CC protocol is devised for second-order general nonlinear MAS without leaders. Finally, the usefulness of the derived results is validated by illustrative examples.

This paper investigates the stability issue of cluster synchronization manifold in networks of diffusively-coupled nonlinear system with directed topology. It is assumed that each cluster subdigraph is strongly connected and contains only cooperative interactions, but there may exist competitions among nodes of different clusters. In the case that the clusters meet the cluster-input-equivalence condition and each nonlinear system has a forward invariant set over which the system possesses a bounded Jacobian, some local stability conditions of cluster synchronization are derived. These conditions are expressed as an inequality of the matrix measure of the system’s Jacobian matrix, the matrix measure of a reduced-order matrix about the digraph among the clusters, the coupling strength, and some eigenvalue conditions of the subdigraphs. Finally, the theoretical findings are validated by two numerical examples about coupled Lorenz-like system and coupled Hopfield neural network, respectively.

This article is concerned with the output feedback security control of a class of high-order nonlinear-interconnected systems with denial-of-service (DoS) attacks, nonlinear dynamics, and exogenous disturbances. First, extreme learning machine (ELM) and adaptive techniques are adopted to approximate the unknown nonlinearities. Then, novel adaptive ELM-based nonlinear state observers with adaptive compensation functions are developed to estimate the unmeasurable states during DoS attacks under the influence of the disturbances. Further, by combining with the backstepping control and filtering techniques, adaptive ELM-based controllers are proposed to achieve uniformly ultimately bounded results based on the observation and adaption control signals under the influence of DoS attacks, nonlinear dynamics, and exogenous disturbances. Comparative studies are carried out to validate the effectiveness of the developed ELM-based adaptive observation and control strategies for two interconnected power systems.

In this article,${H}_{\infty }$control for stochastic singular time-varying delay systems under arbitrarily variable samplings is addressed via designing a sampled-data controller. The first and foremost, a novel time-dependent discontinuous Lyapunov–Krasovskii (L–K) functional is built, which takes good advantage of the factual sampling pattern’s available properties. Then, based on the refined input delay method by utilizing the constructed time-dependent L–K functional, the free-weighting matrix method, and the auxiliary vector function approach are adopted to develop conditions ensuring the stochastic admissibility for the studied stochastic singular systems with time-varying delays. On the basis of the derived conditions, the sampled-data${H}_{\infty }$control issue is tackled, and an unambiguous expression for the sampled-data controller design method is obtained. Finally, simulation examples manifest that our proposed results are correct and effective.

In this article, inspired by the Halanay inequality, we study stability of sampled-data systems with packet losses by proposing a nonuniform sampling interval approach. First, a sampled-data controller with an exponential gain is put forward to reduce conservatism. We obtain the sufficient condition for linear sampled-data systems to be exponentially stable by extending the famous Halanay inequality to sampled-data systems. The obtained sufficient conditions indicate that the maximal-allowable bound of sampling intervals is determined by the constant terms in the Halanay inequality, and the decay rate is presented in the form of a Lambert function. Compared with some existing results on the stability of sampled-data systems by using the Gronwall-Bellman Lemma, the conservatism induced by the exponential term via the Gronwall-Bellman Lemma can be reduced to some extent. Considering the phenomenon of packet losses, a new lemma is further proposed to generalize the proposed Halanay-like inequality. The results derived by the new lemma permit that there exist some sampling intervals with the upper bound violating the desired condition of the Halanay-like inequality. This permits us to establish exponential stability in significant cases that do not satisfy the Halanay-like inequality needed in the previous results. Finally, the sampled-data local exponential stability is investigated for nonlinear systems with strong nonlinearity.

As a fundamental structure in real-world networks, in addition to graph topology, communities can also be reflected by abundant node attributes. In attributed community detection, probabilistic generative models (PGMs) have become the mainstream method due to their principled characterization and competitive performances. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to the existing PGMs that require attributes to be categorical or Gaussian distributed. Based on the block model of graph structure, our model incorporates the attribute by describing its effect on node popularity. To characterize the effect quantitatively, we analyze the community detectability for our model and then establish the requirements of the node popularity term. This leads to a new scheme for the crucial model selection problem in choosing and solving attributed community detection models. With the model determined, an efficient algorithm is developed to estimate the parameters and to infer the communities. The proposed method is validated from two aspects. First, the effectiveness of our algorithm is theoretically guaranteed by the detectability condition. Second, extensive experiments indicate that our method not only outperforms the competing approaches on the employed datasets, but also shows better applicability to networks with various node attributes.

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

In this article, the distributed finite-time optimization problem is investigated for second-order multiagent systems with unknown velocities, disturbances, and quadratic local cost functions. To solve this problem, by combining finite-time observers (FTOs), the homogeneous systems theory, and distributed finite-time estimator techniques together, an output feedback-based feedforward-feedback composite distributed control scheme is proposed. Specifically, the control scheme consists of three parts. First, some FTOs are developed for the agents to estimate their unknown velocities and the disturbances together. Second, based on the velocity and disturbance estimates, the homogeneous system theory, and some global information on all the local cost functions' gradients, Hessian matrices, and the velocity estimates, a kind of centralized finite-time optimization controllers is designed. Third, by designing some distributed finite-time estimators and using their estimates to replace the global terms employed in the centralized optimization controllers, the distributed finite-time optimization controllers are derived. These controllers achieve the distributed finite-time optimization goal. Simulations illustrate the effectiveness and superiority of the proposed control scheme.

This article aims to address the multiobjective scaled consensus problem for a class of heterogeneous multiagent systems (MASs) with communication delays: 1) a distributed hybrid control strategy is proposed which only requires local communication between neighbors to solve the consensus problem; 2) in order to overcome the technical difficulties caused by communication delays, a novel augmentation method is presented to model the error system with communication delays as a delay-free error system. A switching-based time-varying Lyapunov function is introduced to deal with switching impulses of the augmented system; 3) as an application of the proposed distributed control scheme, a new algorithm for designing the hybrid secondary control of the DC microgrid against communication delays is derived; and 4) the effectiveness of the proposed control scheme is illustrated by numerical examples and several case studies in islanded DC microgrid.

In this article, the active fault-tolerant control (FTC) issue is addressed for switched linear parameter varying (LPV) systems in the discrete-time domain. A general dwell-time property is considered in the system setup to govern the switching dynamics between subsystems, and the parameter variations within each subsystem are subjected to the polytopic uncertainties. The main innovation of the developed active FTC approach is to effectively cope with switched LPV models where both input and output matrices can be of parameter-dependent form. By virtue of a novel Lyapunov function approach depending on both dwell time and parameter variations, the joint task for fault detection, estimation, and compensation is fulfilled via an integrated design scheme. The input-to-state stability (ISS) conditions are obtained for the augmented faulty system satisfying a given ISS-gain performance requirement. The desired effect of active FTC is achieved in three critical steps: 1) construct a fault detection filter in terms of the$\mathcal {H}_{\infty }$norm; 2) design an observer-based estimator to estimate the system states and faults simultaneously; and 3) synthesize a fault-tolerant controller with the LPV structure using the estimated states and faults for fault compensation. Finally, an application example is utilized to illustrate the efficiency and availability of the proposed control approach.

To address the conflict caused by the fixed sampled period in the multi-sampled multiband-structured subband adaptive filter (MS-MSAF), the adaptive convex combination of two MS-MSAFs is proposed in this paper, in which the individual filters independently run with different sampled periods. Moreover, the mean behavior of the convex-combined two MS-MSAF algorithm is also studied. In addition, the convex-combined two MS-MSAF algorithm with periodic feedback is also developed to further enhance the convergence performance. Finally, the validity of the proposed adaptive filtering algorithms together with their theoretical analyses is supported by computer simulations.

In this paper, a robust diffusion average-estimate normalized least mean square (NLMS) algorithm is proposed to tackle the impulsive noise in adjacent node communication links over distributed networks. Compared with the diffusion NLMS algorithm, the key point of the new algorithm is that it introduces two additional steps: robust detection and average estimation, thus greatly reducing the negative effects of the impulsive link noise. In addition, the mean stability of the proposed robust diffusion average-estimate NLMS algorithm is analyzed. Finally, Monte-Carlo simulation results demonstrate the effectiveness of the proposed robust diffusion algorithm.

In this paper, the stability and stabilization issues are investigated for a class of discrete-time switched piecewise affine systems with modal average dwell time switching. Both the autonomous (state-partition-dependent) switching and the controlled (modal average dwell time) switching appear concurrently in the studied system. At first, the exponential stability criterion is derived using the proposed piecewise quadratic Lyapunov function approach. Then the state feedback controller in the piecewise affine form is designed to achieve stabilization for the resulting closed-loop switched piecewise affine system. In the end, the effectiveness and accuracy of the theoretical findings are testified via a numerical example.

Cross-domain lithology identification (CDLI) is a common case in lithology identification, which aims to train a machine learning model using the logging data of an interpreted well to predict the lithology of another uninterpreted well. Compared with the general lithology identification problem, the CDLI problem is more challenging for two reasons: the data distribution shift between the wells, and the expensive label acquisition on the uninterpreted well. To tackle these issues, we propose a novel framework that embeds active learning (AL) and domain adaptation into lithology identification. The proposed framework is composed of two components: an AL algorithm that selects the most uncertain and diverse target samples to query their real labels, and a source reweighting method that leverages the target labels to reduce data distribution discrepancy. Experimental results on two real-world data sets demonstrate that the proposed method can more effectively suppress the performance degradation caused by the data distribution shift than the baselines, with fewer target label queries.

This paper introduces two combined-sample multiband-structured subband adaptive filters (MSAFs). In the design, an adaptive convex combination scheme of two self-reliant multi-sampled MSAF (MS-MSAF) with different sampled periods is firstly developed, which leads to the so-called CTMS-MSAF algorithm. Secondly, based on an adaptive filter, the combined-sample MS-MSAF (CMS-MSAF) algorithm is proposed via designing a time-varying sampled period, which possesses lower computational complexity than the former. Then, the convergence behaviors of the CTMS-MSAF and CMS-MSAF algorithms are investigated using standard mean-square deviation analysis. Finally, the simulation study in the system identification and acoustic echo cancellation applications shows that at the same steady-state error, the CMS-MSAF method provides a faster convergence rate than the improved convex combination of two MSAFs, combined-step-size MSAF and CTMS-MSAF algorithms.

In this paper, a novel saturation-based control method is proposed for high-order sliding mode (HOSM) dynamics with upper-triangular nonlinearities. Firstly, a new HOSM dynamics with upper-triangular nonlinearities is constructed based on the traditional HOSM dynamics, so as to reduce the uncertainties in the control input channel. Then, a HOSM controller is established by means of the adding a power integrator method such that the new HOSM dynamics is locally stabilized. Finally, the saturation-based controller is constructed by a combination of the saturation technique and the local HOSM controller to guarantee that the sliding variables can converge into a domain of attraction in a finite time and retain inside it thereafter. The rigorous stability analysis is made by the Lyapunov theory. A simulation example is also given to demonstrate the effectiveness of the proposed method.

In this paper, the output feedback based periodic event-triggered control problem is considered for a class of nonminimum-phase nonlinear systems via dynamic event-triggering mechanism. When only the sampled-data output is known, a new periodic event-triggered control method is proposed via output feedback and the control input updates based on a discrete-time dynamic event-triggering condition. Despite the unstable zero dynamics, the proposed control method can asymptotically stabilize the hybrid control systems by closing the loop only when it is necessary. In contrast to the continuous-time static one, the proposed dynamic triggering condition has several advantages including the easier digital implementation and the larger average inter-event time interval. The delicate analysis gives the explicit expression of the maximum allowable sampling period, and the global asymptotic stability can be achieved for the hybrid control systems. Finally, the effectiveness of the proposed periodic event-triggered control method is verified by a numerical simulation.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods. However, the model trained on the interpreted logging data is not effective in predicting new exploration well due to the data distribution discrepancy. In this article, we aim to train a lithology identification model for the target well using a large amount of source-labeled logging data and a small amount of target-labeled data. The challenges of this task lie in three aspects: 1) the distribution misalignment; 2) the data divergence; and 3) the cost limitation. To solve these challenges, we propose a novel active adaptation for logging lithology identification (AALLI) framework that combines active learning (AL) and domain adaptation (DA). The contributions of this article are three-fold: 1) the domain-discrepancy problem in intelligent logging lithology identification is first investigated in this article, and a novel framework that incorporates AL and DA into lithology identification is proposed to handle the problem; 2) we design a discrepancy-based AL and pseudolabeling (PL) module and an instance importance weighting module to query the most uncertain target information and retain the most confident source information, which solves the challenges of cost limitation and distribution misalignment; and 3) we develop a reliability detecting module to improve the reliability of target pseudolabels, which, together with the discrepancy-based AL and PL module, solves the challenge of data divergence. Extensive experiments on three real-world well-logging datasets demonstrate the effectiveness of the proposed method compared to the baselines.

In this article, the hidden Markov model (HMM)-based fuzzy control problem is addressed for slow sampling model nonlinear Markov jump singularly perturbed systems (SPSs), in which the general transition and mode detection information issue is considered. The general information issue is formulated as the one with not only the transition probabilities (TPs) and the mode detection probabilities (MDPs) being partly known but also with the certain estimation errors existing in the known elements of them. This formulation covers the cases with both the TPs and the MDPs being fully known, or one of them being fully known but another being partly known, or both them being partly known but without the certain estimation errors, which were considered in some previous literature. By utilizing the HMM with general information, some strictly stochastic dissipativity analysis criteria are derived for the slow sampling model nonlinear Markov jump SPSs. In addition, a unified HMM-based fuzzy controller design methodology is established for slow sampling model nonlinear Markov jump SPSs such that a fuzzy controller can be designed depending on whether the fast dynamics of the systems are available or not. A numerical example and a tunnel diode circuit are finally used to illustrate the validity of the obtained results.

In this article, the consensus problem of linear systems is revisited from a novel geometric perspective. The interaction network of these systems is assumed to be piecewise fixed. Moreover, it is allowed to be disconnected at any time but holds a quite mild joint connectivity property. The system matrix is marginally stable and the input matrix is not of full-row rank. By directly examining the subspace determined by the network, we first establish convergence by resorting to an observability condition. Then, according to joint connectivity, we are able to extend this convergence uniformly to the entire orthogonal complement of the consensus manifold. In this way, we work out the necessary and sufficient condition for exponential consensus. It turns out that, with a suitably designed feedback matrix, exponential consensus can be realized globally and uniformly if and only if a jointly (δ,T) -connected condition and an observability condition relying only on the system and input matrices are satisfied. We also characterize the lower bound of the convergence rate. Simple yet effective examples are presented to illustrate the findings.

In this article, the problem of cooperative global robust practical output regulation is examined for uncertain nonlinear multiagent systems in a lower triangular form via event-triggered control. The problem is dealt with in three steps. At the first step, a decentralized internal model is constructed based on the lower triangular form such that the regulation issue is translated to a stabilization one. Next, a nonlinear decentralized state-feedback controller is designed to achieve input-to-state stabilization with the sampling error as the input. In the third step, a simple event-triggering mechanism is embedded in the controller to achieve an event-based controller redesign. An illustrative example is presented to verify the theoretical results.

The problem of fault-tolerant adaptive fuzzy tracking control against actuator faults is investigated in this article for a type of uncertain nonaffine fractional-order nonlinear full-state-constrained multi-input-single-output (MISO) system. By means of the existence theorem of the implicit function and the intermediate value theorem, the design difficulty arising from nonaffine nonlinear terms is surmounted. Then, the unknown ideal control inputs are approximated by using some suitable fuzzy-logic systems. An adaptive fuzzy fault-tolerant control (FTC) approach is developed by employing the barrier Lyapunov functions and estimating the compounded disturbances. Moreover, under the drive of the reference signals, a sufficient condition ensuring semiglobal uniform ultimate boundedness is obtained for all the signals in the closed-loop system, and it is proved that all the states of nonaffine nonlinear fractional-order systems are guaranteed to remain inside the predetermined compact set. Finally, two numerical examples are provided to exhibit the validity of the designed adaptive fuzzy FTC approach.

This article discusses the issue of disturbance rejection and anti-windup control for a class of complex systems with both saturating actuators and diverse types of disturbances. At the input port, to better characterize those irregular disturbances, exogenous dynamic neural network (DNN) models with adjustable weight parameters are first introduced. A novel disturbance observer-based adaptive control (DOBAC) technique is then established, which realizes the dynamic monitoring for the unknown input disturbance. To handle the system disturbance with a bounded norm, the attenuation performance is concurrently analyzed by optimizing the$L_{1}$gain index. Moreover, the PI-type dynamic tracking controller is proposed by integrating the polytopic description of the saturating input with the estimation of the input disturbance. The favorable stability, tracking, and robustness performances of the augmented system are achieved within a given domain of attraction by employing the convex optimization theory. Finally, using DNN-based modeling for three kinds of different irregular disturbances, simulation studies for an A4D aircraft model are conducted to substantiate the superiority of the designed algorithm.

A large number of experiments have proved that the ring structure is a common phenomenon in neural networks. Nevertheless, a few works have been devoted to studying the neurodynamics of networks with only one ring. Little is known about the dynamics of neural networks with multiple rings. Consequently, the study of neural networks with multiring structure is of more practical significance. In this article, a class of high-dimensional neural networks with three rings and multiple delays is proposed. Such network has an asymmetric structure, which entails that each ring has a different number of neurons. Simultaneously, three rings share a common node. Selecting the time delay as the bifurcation parameter, the stability switches are ascertained and the sufficient condition of Hopf bifurcation is derived. It is further revealed that both the number of neurons in the ring and the total number of neurons have obvious influences on the stability and bifurcation of the neural network. Ultimately, some numerical simulations are given to illustrate our qualitative results and to underpin the discussion.

Singularly perturbed systems have found widespread applications in practice. The existing results on singularly perturbed systems mainly focused on the Lyapunov asymptotic stability, which are unable to deal with the cases that the system states cannot exceed a given threshold during a fixed time interval. This article addresses the finite-time fuzzy control issue for discrete-time nonlinear singularly perturbed systems with input constraints. The aim is to guarantee the boundedness of the states of singularly perturbed systems during a finite-time interval. Based on the matrix inequality technique, some conditions are established to guarantee the finite-time boundedness of the fuzzy singularly perturbed systems, where the singularly perturbed parameter is independent so as to avoid the ill-conditioned problem caused by the small singularly perturbed parameter. The gains of the finite-time fuzzy controller can be obtained by solving some singularly perturbed parameter independent linear matrix inequalities. Finally, the proposed finite-time fuzzy controller design approach for nonlinear singularly perturbed systems is illustrated via the Van der Pol circuit.

The sampled-data stabilization problem of nonlinear stochastic singular systems on the basis of the Takagi–Sugeno fuzzy models under variable samplings is discussed in this article. A new piecewise Lyapunov–Krasovskii functional is constructed, which can capture the actual sampling mode’s available features more fully, and an enhanced input-delay method is presented. By using the proper augmented scheme based on the auxiliary vector function, the new mean square admissibility criteria are derived by making good use of the convex combination techniques and the free weighting matrix approach. It is shown that the obtained results in this article contain less conservatism when compared with the existing ones. The superiority and correctness of our results are verified by an application example of a truck–trailer model.

This article presents the observer-based event-triggered adaptive hybrid fuzzy dynamic surface control strategy for a category of uncertain nonstrict-feedback fractional-order nonlinear multi-input multi-output systems, including unknown time-varying delays and actuator faults. First, an adaptive hybrid fuzzy state observer and a serial-parallel estimation system are constructed to estimate the unmeasured system states and incorporate them into the control design scheme, respectively, where some appropriate fuzzy logic systems are introduced to approximate the unknown nonlinear functions. According to the dynamic surface control technique, the designed adaptive fuzzy control approach can surmount the deficiency of “complexity explosion.” Then, an observer-based adaptive event-triggered control algorithm is developed by constructing the Lyapunov–Krasovskii functionals and estimating the compounded disturbances. Furthermore, it is proved that under the drive of the reference signals, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior can be successfully excluded. Finally, an example with numerical simulations is utilized to exhibit the applicability of the obtained observer-based event-triggered adaptive fuzzy control approach.

With the popularization of network equipment and the rapid development of information technology, the scale and complexity of wireless sensor networks (WSNs) continue to expand. How to effectively locate link failures has become a challenging problem in WSNs. In this paper, we propose a novel method of locating link failures based on distributed cluster consensus protocol and graph decomposition technique. In our method, the initial data is injected into sensor nodes for distributed interactions, and then link failures can be located by observing and comparing the output data of the nodes. The proposed method is suitable for the situations with both single-link failure and multi-link failures, and has no limitations on the number, distribution and correlation of link failures. Necessary and sufficient conditions are provided to guarantee the accuracy of the proposed method in locating link failures. At last, the effectiveness of the proposed method is verified by both real and simulation experiments.

This article addresses the$H_{\infty }$control issue for Takagi–Sugeno (T–S) fuzzy systems via an event-triggered control mechanism. First, a hybrid event-triggered control strategy combined with an adaptively adjusted approach is proposed, by which the data transmission can be effectively reduced, and meanwhile, the Zeno behavior is excluded. Then, a discontinuous Lyapunov function is constructed for the resulting hybrid event-triggered control system, and a novel lemma is developed for studying the exponential stability and disturbance attenuation performance. By utilizing a time-varying variable to manage the error during the interevent intervals together with using the newly proposed lemma, sufficient criteria are established for the stability and control design of the event-triggered T–S fuzzy system with the imperfect premise matching. Finally, numerical simulations are presented to illustrate the usefulness and advantages of the obtained theorems.

This article is concerned with the robust adaptive control circuit design for pulse wide modulation (PWM)-based dc–dc buck converters with load variations and exogenous disturbances. A robust adaptive perturbation rejection control strategy is first developed to suppress time-varying and state-dependent perturbations, which are composed of load variations and disturbances. Then, equivalent analog control circuits of the adaptive control strategy are implemented on the basis of the circuit theory. Bounded tracking of the closed-loop converter system in the presence of perturbations is achieved based on the Lyapunov stability theorem. Simulations and experimental results are provided to validate the efficiency of the proposed adaptive perturbation rejection control strategy in a dc–dc buck converter system.

In the electronic nose (e-nose), a stable feature representation of the gas sensor’s response is a key step to realize subsequent odor identification algorithms. However, the noises in gas sensors hinder the acquisition of such features. In order to solve this problem, this article proposes a stable feature extraction algorithm which takes the impulse response of the e-nose system as the feature. The impulse response is estimated from a nonparametric model constrained by a multiscale wavelet kernel regularization matrix. The kernel regularization matrix equips the proposed feature extraction method with an ability in resistance to random noise. A numerical experiment proves that compared with single-scale kernel regularization, the use of multiscale wavelet kernel helps to achieve more stable and accurate impulse response estimation. Then, a field experiment is conducted to demonstrate the performance of the proposed features. This experiment aims to identify four different whiskies measured by a self-designed e-nose with four commercial gas sensors. Under the framework of transfer learning, the classification result based on the proposed features outperforms those using other considered features. The accuracy of whisky identification reaches 92.00%, showing a good potential of applying the proposed feature representations in the area of e-noses.

The dynamics of neural networks has been widely concerned by scholars. However, most of the previous results on dynamical bifurcations are limited to few nodes coupling neural networks which modeled by differential equations with integer-order derivative, and few efforts have been contributed to studying the bifurcation behaviors of large-scale fractional-order neural networks. Furthermore, the structural characteristics of networks are also of great research value. Among them, the ring structure is a common phenomenon in neural networks. Although there are few papers on the bifurcation analysis of ring-structured neural networks recently, they consider only the case of a single ring. In this article, the dynamical analysis and design for a class of large-scale-delayed fractional-order neural networks with multiple rings and hub structure are investigated. First, the time delay is considered to be the bifurcation parameter and the formula of Coates’ flow graph is adopted to obtain the characteristic equation of large-scale networks. Second, by analyzing the complex radial and circular connections of neurons, the delay-induced Hopf bifurcation sufficient conditions for the neural network are established. Finally, the theoretical results are substantiated by a number of numerical simulation experiments and the relationships between the onset of bifurcation and the fractional order, the number of neurons, and the number of rings are revealed.

This article proposes static/dynamic event-triggered and self-triggered discrete gain scheduled control with a designable parametric minimal interevent time (MIET) to achieve semiglobal stabilization of linear systems with input constraints. First, a novel static event-triggered discrete gain scheduled control, which can improve the control performance and simultaneously save the communication resources, is proposed by utilizing the properties of the parametric Lyapunov equation (PLE). Moreover, the static self-triggered mechanism, in which the next control law updates based on the previous triggered states, is also designed to avoid the monitoring of all states. In order to further increase the interevent times (IETs), the corresponding dynamic event-triggered and self-triggered discrete gain scheduled control are designed, respectively. All the proposed algorithms can not only avoid the Zeno phenomenon but also provide a designable parametric MIET. This allows to easily find a tradeoff between the IETs and the control performance by adjusting the only design parameter. In addition, by exploiting the properties of the PLE, the designed algorithms avoid the complicated relationship between the MIET and the system matrices. In some cases, the MIET can totally avoid the relationship with the system itself and be designed as an arbitrarily large bounded constant. Finally, applications to the spacecraft rendezvous system show the effectiveness of the established algorithms.

In wireless communication systems, the conventional constant modulus algorithm incurs artificial error and steady state misadjustment. In this study, an improved constant modulus algorithm (ICMA) and its generalized form are proposed for blind equalization. The ICMA utilizes the clustering function of Gaussian function to efficiently suppress this artificial error and steady state misadjustment at the cost of reduction in the sample usage rate. Moreover, the generalized form of the ICMA is developed to ensure the sample usage rate while the good performances of the ICMA are maintained. Simulation results illustrate better equalization performances of the ICMA and generalized ICMA, as compared to the classical constant modulus algorithm.

This paper is concerned with controller design for high-order sliding mode (HOSM) dynamics with upper-triangular uncertainties. First, we design an HOSM controller by means of the adding a power integrator method in order to locally stabilize the HOSM dynamics with upper-triangular uncertainties. On this basis, by combining the saturated technique and the local HOSM controller together, we devise a saturated-like controller in order to ensure the finite-time convergence of the sliding variables into a domain of attraction without an escape from the domain thenceforth. The stability results are established based on the Lyapunov theory. The efficiency of the designed saturated-like HOSM controller is validated by a numerical example.

This paper focuses on the event-driven sliding mode control (SMC) problem of discrete-time two-dimensional (2D) Roesser systems. First, the horizontal and vertical sliding surface functions are constructed for the considered 2-D systems, respectively, and sufficient conditions are proposed to guarantee the asymptotical stability of the resultant 2-D sliding mode dynamics. Second, the sliding mode controller is designed to ensure the reachability of the sliding mode in a finite region and is maintained there all the time. Third, the event-triggered strategy is further incorporated to implement the event-driven SMC scheme for better resource utilisation. Particularly, the eventdriven rule is established in combination with the reachability of the sliding mode, and meanwhile the event-driven instants are determined to perform the event-driven SMC for the addressed 2-D systems. Finally, one verification example is given to show the effectiveness of the proposed SMC design method.

In this paper, an investigation is launched into the compressive diffusion strategy in the presence of noisy communication links so as to develop the compressive diffusion double normalized least mean square (NLMS) algorithm. Based on the single update global weight-error model, an analytical formulation of the transient and steady-state results is made for the compressive diffusion double NLMS algorithm. These analytical results are instrumental to making a better understanding of the mean- square performance of the compressive diffusion strategy against link noises. Lastly, simulation study is carried out to validate the performance of the developed compressive diffusion double NLMS algorithm over adaptive networks subject to link noises.

Robust network infrastructures are essential for the delivery of vital services for our daily lives. With the widespread cyber-attacks on them, measuring the robustness of these networks has become an important issue. In recent years, many robustness metrics have been proposed for this purpose. Among them, a metric called ‘R’ has received wide attention, and several variants have been proposed. These variants include a metric called Communication Robustness (CR) and the betweenness and closeness variants to both R and CR. However, no evaluations about the correlations among these variants have been made to shed light on their necessity and effectiveness. Addressing this research gap, this paper makes the following contributions. First, we measure the correlations between R and CR to verify that CR is valid to exist due to its unique perspective although it correlates closely with R. Second, we measure the correlations between R and its betweenness/closeness variants to show that they are quite different, and the same is observed for CR and its betweenness/closeness variants. Finally, we propose a new robustness metric called Simplified Communication Robustness (SCR), which simplifies the calculation of CR while working almost the same as CR.

In this letter, considering the lack of core and drilling cuttings, an interpretable semisupervised classification method (ISSCM) under multiple smoothness assumptions is proposed and applied to lithology identification. The contribution is threefold. First, the novel semisupervised learning algorithm is developed based on the decision tree, the interpretability of which is highly beneficial to solve risk-aware problems. Second, both smoothness in the feature space and depth is utilized to generate pseudo-labels for the unlabelled data by using label propagation. Third, an algorithm to approximate the optimal affinity matrix is added to avoid degradation rendered by inappropriate manual settings under multiple smoothness assumptions. All these contributions could yield a classification model that is interpretable, accurate, and insusceptible to imprecise empirical settings. In the experiment, the proposed method is applied to lithology identification and verified by real-world data.

In this article, the sliding mode control (SMC) design is studied for a class of stochastic switching systems subject to semi-Markov process via an adaptive event-triggered mechanism. Network-induced communication constraints, semi-Markov switching parameters, and uncertain parameters are considered in a unified framework for the SMC design. Due to the constraint of measuring transducers, the system states always appear with unmeasurable characteristic. Compared with the traditional event-triggered mechanism, the adaptive event-triggered mechanism can effectively reduce the number of triggering than the static event-triggered mechanism. During the data transmission of network communication systems, network-induced delays are characterized from the event trigger to the zero-order holder. The aim of this work is to design an appropriate SMC law based on an adaptive event-triggered communication scheme such that the resulting closed-loop system could realize stochastic stability and reduce communication burden. By introducing the stochastic semi-Markov Lyapunov functional, sojourn-time-dependent sufficient conditions are established for stochastic stability. Then, a suitable SMC law is designed such that the system state can be driven onto the specified sliding surface in a finite-time region. Finally, the simulation study on boost converter circuit model (BCCM) illustrates the effectiveness of the theoretical findings.

This paper proposes two kinds of algorithms to achieve privacy-preserving consensus of multi-agent systems over undirected graphs via node decomposition mechanism and homomorphic cryptography technique. Based on the number of neighboring nodes ( |\mathscr Ni|), every agent is decomposed into |\mathscr Ni| subagents, which are connected as a chain graph. Note that every subagent connects one and only one non-homologous subagent (generated by different agents). Information interaction between non-homologous subagents is encrypted by a homomorphic cryptography algorithm, and homologous subagents exchange information directly. In this regard, the proposed node decomposition mechanism enhances the privacy of the initial values without increasing the computational complexity of encryption. The first privacy-preserving algorithm can achieve the accurate average consensus, which means that the agreement value of every subagent is consistent with the original average consensus value. The second algorithm studies the privacy-preserving scaled consensus problem without a priori knowledge about the underlying graph. Although the final convergence values of subagents do not keep exactly the same, homologous subagents can compute the original group decision value by resorting to the product of the limit value and agent's degree. Importantly, this algorithm also guarantees the privacy of group decision value of the whole system. Besides, it is proved that the privacy of the initial value can be preserved if the agent has at least one neutral neighbor.

This paper studies the composite refined anti-disturbance control problem of a 2-degree-of-freedom robot arm system modeled by a semi-Markov jump system with multiple disturbances, which includes harmonic disturbances with unknown frequency and amplitude as well as energy bounded disturbances. Firstly, the semi-Markov jump system model is proposed to construct a novel linear model of the 2-degree-of-freedom robot arm subject to two types of disturbances. Next, in order to estimate the uncertain harmonic disturbance, a novel higher order disturbance observer is introduced to convert the uncertain harmonic disturbance into some parameter uncertainty and then estimate the parameter uncertainty. In addition, a corresponding composite anti-disturbance control scheme is formulated to reject and attenuate the above two types of disturbances, respectively. Furthermore, sufficient conditions that can guarantee that the system is stochastically stable are given. Finally, a simulation study of the obtained model is carried out to illustrate the validity of the composite control design method proposed in this paper.

This article addresses the bipartite tracking consensus for a set of mobile autonomous agents over directed cooperation-competition networks. Here, cooperative and competitive interactions among the agents are described by positive and negative edges of the directed network topology, respectively. Both fixed and switching network topologies are considered. For the case with fixed network topology, the matrix product technique is utilized to derive the convergence result. For the case with switching network topologies, some key results related to the composition of binary relations are the main technical tools of analyzing the error system. In addition, the upper bound for the spectral radius of the system matrix is given to ensure the convergence of the system even if the single uncoupled system is strictly unstable. The applicability of the derived results is verified through two simulation experiments.

In this article, the problem of disturbance rejection control is studied for discrete-time systems within an event-driven control framework. Through a predefined event-driven scheduler, both full- and reduced-order extended state observer (ESO)-based output feedback controllers are designed. With the proposed ESOs, both the disturbance and the system states are estimated, and the controllers are constructed with the estimated disturbance and states. Then, the stability and disturbance rejection analyses are conducted. It can be found that with the established event-driven control approaches, the updating frequency of the controller can be prominently reduced, and the disturbance can also be compensated in the output channels of the systems. Finally, the validity of the established control approaches is illustrated by the numerical simulations.

Considering the fact that in the real world, a certain agent may have some sort of advantage to act before others, a novel hierarchical optimal synchronization problem for linear systems, composed of one major agent and multiple minor agents, is formulated and studied in this article from a Stackelberg-Nash game perspective. The major agent herein makes its decision prior to others, and then, all the minor agents determine their actions simultaneously. To seek the optimal controllers, the Hamilton-Jacobi-Bellman (HJB) equations in coupled forms are established, whose solutions are further proven to be stable and constitute the Stackelberg-Nash equilibrium. Due to the introduction of the asymmetric roles for agents, the established HJB equations are more strongly coupled and more difficult to solve than that given in most existing works. Therefore, we propose a new reinforcement learning (RL) algorithm, i.e., a two-level value iteration (VI) algorithm, which does not rely on complete system matrices. Furthermore, the proposed algorithm is shown to be convergent, and the converged values are exactly the optimal ones. To implement this VI algorithm, neural networks (NNs) are employed to approximate the value functions, and the gradient descent method is used to update the weights of NNs. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

In this article, by introducing a signed graph to describe the coopetition interactions among network nodes, the mathematical model of multiple memristor-based neural networks (MMNNs) with antagonistic interactions is established. Since the cooperative and competitive interactions coexist, the states of MMNNs cannot reach complete synchronization. Instead, they will reach the bipartite synchronization: all nodes' states will reach an identical absolute value but opposite sign. To reach bipartite synchronization, two kinds of the novel node- and edge-based adaptive strategies are proposed, respectively. First, based on the global information of the network nodes, a node-based adaptive control strategy is constructed to solve the bipartite synchronization problem of MMNNs. Secondly, a local edge-based adaptive algorithm is proposed, where the weight values of edges between two nodes will change according to the designed adaptive law. Finally, two simulation examples validate the effectiveness of the proposed adaptive controllers and bipartite synchronization criteria.

Recently, the dynamics of delayed neural networks has always incurred the widespread concern of scholars. However, they are mostly confined to some simplified neural networks, which are only made up of a small amount of neurons. The main cause is that it is difficult to decompose and analyze generally high-dimensional characteristic matrices. In this article, for the first time, we can solve the computing issues of high-dimensional eigenmatrix by employing the formula of Coates flow graph, and the dynamics is considered for a bidirectional neural network with super-ring structure and multiple delays. Under certain circumstances, the characteristic equation of the linearized network can be transformed into the equation with integration element. By analyzing the equation, we find that the self-feedback coefficient and the delays have significant effects on the stability and Hopf bifurcation of the network. Then, we achieve some sufficient conditions of the stability and Hopf bifurcation on the network. Furthermore, the obtained conclusions are applied to design a standardized high-dimensional network with bidirectional ring structure, and the scale of the standardized high-dimensional network can be easily extended or reduced. Afterward, we propose some designing schemes to expand and reduce the dimension of the standardized high-dimensional network. Finally, the results of theories are coincident with that of experiments.

This article focuses on the hybrid effects of memristor characteristics, time delay, and biochemical parameters on neural networks. First, we propose a novel neuron system with memristor and time delays in which the memristor is characterized by a smooth continuous cubic function. Second, the existence of equilibria of this type of neuron system is examined in the parameter space. Sufficient conditions that ensure the stability of equilibria and occurrence of pitchfork bifurcation are given for the memristor-based neuron system without delay. Third, some novel criteria of the addressed neuron system are constructed for guaranteeing the delay-dependent and delay-independent stability. The specific conditions are provided for Hopf bifurcations, and the properties of Hopf bifurcation are ascertained using the center manifold reduction and the normal form theory. Moreover, there exists a phenomenon of bistability for the delayed memristor-based neuron system having three equilibria. Finally, the effectiveness of the theoretical results is demonstrated by numerical examples.

This paper is concerned with the robust constrained control problem for a class of fault-tolerant time-varying systems against actuator faults and input amplitude saturation. An adaptive technique is proposed to compensate for the impacts of actuator bias faults and partial loss of control effectiveness, as well as to eliminate the effects of unknown time-varying parameters of the systems. An auxiliary system is developed to ensure that the actuator behaves within the limitation of the actuator amplitude. Based on the compensation strategies and auxiliary signals, a novel adaptive fault-tolerant constrained controller is constructed to guarantee the convergence of the system states into a small stability domain in the presence of actuator faults, actuator amplitude limitations, and unknown system parameters. An example of F-18 flight control systems is given to illustrate the proposed procedures and its effectiveness.

This article focuses on the issue of scaled tracking consensus for discrete-time second-order multiagent systems under random packet dropouts, where the cases with a static leader and a dynamic leader are considered, respectively. The scaled tracking consensus means that all agents reach a consensus value determined by the leader but with different scales, and the phenomenon of packet dropout on each communication link is described as a Bernoulli variable independent of other communication links. By virtue of random environment-based scaled consensus algorithms, it is shown how to reconstruct the original system into augmented error systems with random coefficient matrices. With the kind assistance of substochastic matrix and super-stochastic matrix, sufficient conditions for the cases with a static leader and a dynamic leader are derived, respectively. Moreover, computer simulations are performed to demonstrate the dynamics of network agents under random packet dropouts.

This paper develops a new estimate-based dynamic event-triggered output feedback controller for networked control systems subject to nonlinear uncertainties. Specifically, based on the sampled-data, a discrete-time output feedback controller and a discrete-time dynamic event-triggering condition are proposed by the virtue of feedback domination technique. The proposed event-triggered control method is easy to implement in digital computers due to the form of discrete time. Under the proposed dynamic event-triggered control method, the selection regions of the sampling period and the scaling gain are explicitly given to guarantee the global practical/asymptotic stability of the closed-loop system. Finally, two examples are employed to verify the efficiency of the proposed dynamic event-triggered control approach.

This article is devoted to study the cluster synchronization for a kind of complex dynamical networks consisting of nonidentical nonlinear Lur'e systems. Different from general time delays, a proportional delay is taken into consideration in this article, which is a kind of unbounded time-varying delays and thus largely increases the difficulty in network synchronization. In consideration of the topology structures of the Lur'e networks, an effective impulsive pinning controller is proposed, which will be placed on the Lur'e systems having directed paths with those in the other clusters. Considering different functional roles that the impulsive effects play, sufficient criteria for the cluster synchronization of the nonidentically coupled Lur'e dynamical networks are obtained by applying the proportionally delayed impulsive comparison principle, the concept of average impulsive interval, and the extended parameters variation formula. Simultaneously, the exponential convergence rates are successfully estimated with respect to different functions of the impulsive effects. In the end of this article, three numerical simulations are proposed to denote the effectiveness of the control protocols and the theoretical results.

Bifurcation control remains largely unresolved for fractional-order dynamical systems. This article addresses the optimal control issue of bifurcation for a delayed complex networks model with Caputo derivative, where the time delay is selected as the variable parameter. We first devise a fractional-order proportional-integral-derivative (PID) feedback synthesis for regulation of the Hopf bifurcation embedded in a delayed small-world network model with Caputo derivative. Dynamic stability criterion and Hopf bifurcation condition are obtained by carrying out the eigenvalue analysis of the controlled network. The stability range of the parameters of the PID control is evaluated completely for the small-world network. We can optimize the dynamics of stability and bifurcation of small-world networks by manipulating the gain parameters. Finally, we implement some simulations to show the performance of the presented PID scheme. The numerical simulations verify the advantage of the fractional PID controller in bifurcation control.

In this paper, the synchronization issue of memristive neural networks with cooperative and competitive interactions is examined. With a signed graph used to describe coopetition networks, variable substitution is appropriately introduced to convert the equations describing the inertia memristive neural networks (IMNNs) into the first-order differential equations. Then a discontinuous controller is adequately constructed to derive the bipartite synchronization criterion for coupled IMNNs over coopetition networks. Lastly, the validity of the proposed bipartite synchronization criterion is demonstrated by a numerical example.

This paper proposes a new model and its analysis results for time-varying structurally balanced networks. Through model transformations, the system stability problem is converted into the problem of product convergence of infinite sub-stochastic matrices (PCISM). Further, by constructing a new digraph for each interaction topology, the problem of PCISM can be handled by virtue of the properties of row-stochastic matrices. When all leaders belong to only one of the two subgroups, it is shown that the followers that are in the same subgroup as the leaders gradually enter the convex hull formed by the leaders' states, while the others gradually enter the convex hull formed by the leaders' sign-inverted states. And when both subgroups contain leaders, a sufficient algebraic graph condition is established to ensure that all followers can enter the convex hull consisting of the leaders' states and sign-inverted states together. Moreover, it is also found that the followers keep active after entering the convex hulls. Finally, the bipartite containment performance is verified by a simulation test.

In this paper, the problem of unanimity of coupled nonlinear systems over coopetition networks is addressed. Specifically, it is investigated whether all system states of coupled nonlinear systems can reach an identical sign after a threshold of time has passed by, which is known as the unanimity. The switched nonlinear systems are considered in the case of coexistence of cooperative and competitive interactions between coupled nonlinear systems. Some conditions that ensure the unanimity of coupled nonlinear systems are derived by exploiting the properties of eventually positive matrices and Lie bracket. The efficiency of the obtained results is validated by an illustrative example.

Economic dispatch (ED) and unit commitment (UC) problems need to be revisited in order to make a transition from a traditional power system to a smart grid. In this paper, we formulate the ED and UC problems into a unified form, which is also capable of characterizing the infinite horizon UC problem. Based on the formulation, a centralized Q -learning-based optimization algorithm is proposed. The proposed algorithm runs in an online manner and requires no prior information on the mathematical formulation of the actual cost functions, thus being capable of dealing with situations for which such cost functions are too difficult to obtain. Then, the distributed counterpart of the centralized algorithm is developed by relaxing the demand for global information and balancing exploration and exploitation cooperatively in a distributed way. Theoretical analysis of the proposed algorithms is also provided. Finally, several case studies are presented to demonstrate the effectiveness of the proposed algorithms.

This paper addresses the problem of complete stability of delayed recurrent neural networks with a general class of piecewise linear activation functions. By applying an appropriate partition of the state space and iterating the defined bounding functions, some sufficient conditions are obtained to ensure that an n-neuron neural network is completely stable with exactly Πi=1n(2Ki-1) equilibrium points, among which Πi=1nKiequilibrium points are locally exponentially stable and the others are unstable, where Ki(i = 1, .. . ,n) are non-negative integers which depend jointly on activation functions and parameters of neural networks. The results of this paper include the existing works on the stability analysis of recurrent neural networks with piecewise linear functions as special cases and hence can be considered as the improvement and extension of the existing stability results in the literature. A numerical example is provided to illustrate the derived theoretical results.

In this contribution, we propose and investigate the containment control issue for general linear multiagent systems (MASs) under the asynchronous setting, where the network topology is not subjected to any structural restrictions and the roles of the leaders and the followers are entirely determined by the network topology. It is assumed that the interaction time instants of each agent, at which this agent interacts with its neighbors, are independent of the other agents' and can be unevenly distributed. An asynchronous distributed algorithm is proposed to implement the control strategy of linear MASs. The non-negative matrix theory and the composition of binary relations are utilized to handle the asynchronous containment control issue. It is shown that the leaders in each closed and strongly connected component of the network topology will reach a common state and the followers will gradually enter the dynamic convex hull constructed by the leaders. Moreover, it is also proved that the system matrix can be strictly unstable, and the upper bound of the system matrix's spectral radius is explicitly stated. Finally, two simulation examples are also provided to verify the efficacy of our theoretical results.

This paper proposes a new output-based dynamic event-triggered mechanism (ETM) for disturbance rejection control of a class of networked nonlinear uncertain systems subject to additive time-varying disturbance. In the proposed control method, a new robust output feedback controller is first designed based on a generalized proportional-integral observer to attenuate/compensate the undesirable influence of nonlinear uncertainties and disturbances. Different from the static ETM, two new dynamic variables are defined, and thereafter, two kinds of different discrete-time dynamic ETMs are developed only using the sampled-data output signal, such that a better tradeoff between the communication properties and the control properties can be obtained. It is shown that under the proposed control methods, the global bounded stability of the closed-loop hybrid system can be guaranteed by choosing some appropriate parameters. Finally, the numerical simulations of a single link robot arm are conducted to demonstrate the feasibility and efficacy of the proposed control approach.

This paper investigates the problem of quasi-synchronization for a class of discrete-time Lur'e-type switched systems with parameter mismatches and transmission channel noises. Different from the previous studies referring to the persistent dwell-time (PDT) switching signals, the average dwell-time (ADT) constraints combined with the PDT are considered simultaneously in this paper to relax the limitation of dwell-time requirements and to improve the flexibility of the PDT switching signal design. By virtue of the semi-time-varying (STV) Lyapunov function, the synchronization criteria for transmitter-receiver systems in a switched version are obtained to satisfy a prescribed synchronization error bound. An estimate of the synchronization error bound is provided via the reachable set approach and, further, an explicit description of the error bounds is given. Then, sufficient conditions on the existence of STV observers are derived with a predetermined error bound, and the corresponding observer gains are calculated via solving a group of linear matrix inequalities. Finally, the effectiveness and validness of the developed theoretical results are demonstrated via a numerical example.

This article is concerned with the periodic tracking control problem for nonlinear systems. First, the Takagi-Sugeno (T-S) fuzzy model is employed to describe the nonlinear control systems. Second, considering the partly unmeasurable states of the system, a novel fuzzy observer-based repetitive controller, which is the mixed controller of the fuzzy observer-based controller and the fuzzy repetitive controller, is designed to deal with the periodic tracking control problem. To reduce the conservatism and increase the feasible solution space of the stabilization conditions, a new fuzzy relaxed matrix technique is developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Then, the fuzzy Lyapunov functional with an additional separation parameter and the augmented fuzzy matrix technique (the interactions of fuzzy observer subsystems) are proposed such that the delay-dependent stability condition of the closed-loop system in the form of linear matrix inequality is obtained with less conservatism. It is worth noting that due to introducing an additional parameter in the fuzzy Lyapunov functional, the fuzzy controller and fuzzy observer can be separately designed, which largely enhances the flexibility of design with low computational complexity. Finally, three examples are provided to illustrate the effectiveness and less conservatism of the proposed method.

This article considers the resilient consensus problems of discrete-time complex cyber-physical networks under F-local deception attacks. A resilient consensus algorithm, where extreme values received are removed by each node, is first introduced. By utilizing the presented algorithm, a necessary and sufficient condition to ensure resilient consensus in the absence of trusted edges is then provided by means of network robustness. We further generalize the notion of network robustness and present the necessary and sufficient condition for the achievement of resilient consensus in the presence of trusted edges. In addition, we show that through appropriately assigning the trusted edges, the resilient consensus can be reached under arbitrary communication network. Finally, the validity of the theoretical findings is demonstrated by simulation examples.

This paper is concerned with the problem of global exponential passivity for quaternion-valued memristor-based neural networks (QVMNNs) with time-varying delay. The QVMNNs can be seen as a switched system due to the memristor parameters are switching according to the states of the network. This is the first time that the global exponential passivity of QVMNNs with time-varying delay is investigated. By means of a nondecomposition method and structuring novel Lyapunov functional in form of quaternion self-conjugate matrices, the delay-dependent passivity criteria are derived in the forms of quaternion-valued linear matrix inequalities (LMIs) as well as complex-valued LMIs. Furthermore, the asymptotical stability criteria can be obtained from the proposed passivity criteria. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results.

In this paper, the output feedback set stabilization problem for Boolean control networks (BCNs) is investigated with the help of the semi-tensor product (STP) tool. The concept of output feedback control invariant (OFCI) subset is introduced, and novel methods are developed to obtain the OFCI subsets. Based on the OFCI subsets, a technique, named spanning tree method, is further introduced to calculate all possible output feedback set stabilizers. An example concerning lac operon for the bacterium Escherichia coli is given to illustrate the effectiveness of the proposed method. This technique can also be used to solve the state feedback (set) stabilization problem for BCNs. Compared with the existing results, our method can dramatically reduce the computational cost when designing all possible state feedback stabilizers for BCNs.

In this paper, a subband adaptive filtering algorithm is developed over functional link neural network (FLNN) in order to overcome the issue of slow convergence of FLNNs for colored input signals. The basic idea is to introduce a delayless multi-sampled multiband-structured subband FLNN (DMSFLNN). In the proposed DMSFLNN, the principle of minimum disturbance is adopted in every subband with a view to improving the learning capacity of FLNNs. An investigation is made into the mean property of the subband adaptive filtering algorithm, thus establishing a stability condition of the DMSFLNN. Finally, Monte-Carlo simulation study is undertaken to verify the effectiveness of the proposed subband adaptive filtering algorithm.

In this paper, we discuss the anti-synchronization problem of coupled neural networks with time-varying delay, which is also known as the bipartite synchronization problem. Different from the existing literature, the competitive relationship in coupled neural networks is considered, which can be modeled by a signed graph. Due to the signed topology structure, bipartite synchronization is realized when all states of coupled neural networks converge to a final state with identical magnitude but opposite sign. Under the structurally balanced assumption, a pining control scheme is designed and then the bipartite synchronization criterion for coupled neural networks with time-varying delay is obtained. Finally, we present a numerical example to demonstrate the validity of the obtained results.

This paper is concerned with the containment control problem for discrete-time high-order multi-agent systems with switching topologies under the asynchronous setting. Based on the distributed asynchronous consensus protocol using only each agent's own information and its neighbors' partial information, the asynchronous high-order containment control problem with switching topologies is transformed into a product problem of infinite time-varying row-stochastic matrices. Then the properties of row-stochastic matrices are explored to derive a sufficient condition involving graph topologies for asynchronous containment control of high-order multi-agent systems. The theoretical results are finally validated through numerical simulations.

In this paper, a time-scale-dependent coupling scheme for two-time-scale nonlinear complex networks is proposed. According to this scheme, the inner coupling matrices are related to the fast dynamics of individual subsystems, but are no longer time-scale-independent. Designing time-scale-dependent inner coupling matrices is motivated by the fact that the difference of time scales is an essential feature of modular architecture of two-time-scale systems. Under the novel coupling framework, the previous assumption on individual two-time-scale subsystems that the fast dynamics must be exponentially stable can be removed. The idea of time-scale separation is employed to analyze the stability of synchronization error systems via weighted ε -dependent Lyapunov functions. For a given upper bound of the singular perturbation parameter ε , it is proved that the exponential decay rate of the synchronization error can be guaranteed to be independent of the value of ε . In this way, criteria for local and global exponential synchronization are established. The allowable upper bound of ε such that the synchronizability of the considered two-time-scale network is retained can be obtained by solving a set of ε -dependent matrix inequalities. Finally, the efficiency of the proposed time-scale-dependent coupling strategy is demonstrated through numerical simulations.

This paper investigates the consensus tracking problem of second-order nonlinear multiagent systems (MAS) with disturbance and actuator fault by the sliding mode control method. The communication topology of the MAS is directed and only part of the followers have access to the leader's information. First, a discontinuous sliding mode tracking protocol is studied for consensus tracking of the MAS. Second, to address the shortcoming of chattering and difficulty of setting the control gain in the discontinuous protocol, a continuous sliding mode tracking protocol with an adaptive mechanism is developed. The adaptive mechanism will adjust the gain of the control automatically and enable the tracking protocol to work well without prior knowledge of the MAS. Third, the performance of the adaptive sliding mode protocol for consensus tracking of the MAS in the presence of actuator faults of biased fault and partial loss of effectiveness fault is further investigated. Finally, numerical simulations are performed to illustrate the efficiency of the theoretical results.

This paper develops a methodology on sampled-data-based event-triggered active disturbance rejection control (ET-ADRC) for disturbed systems in networked environment when only using measurable outputs. By using disturbance/uncertainty estimation and attenuation technique, an event-based sampled-data composite controller is proposed together with a discrete-time extended state observer. Under the presented new framework, the newest state and disturbance estimates as well as the control signals are not transmitted via the common sensor-controller network, but instead communicated and calculated until a discrete-time event-triggering condition is violated. Compared with the periodic updates in the traditional time-triggered active disturbance rejection control, the proposed ET-ADRC scheme can remarkably reduce the communication frequency while maintaining a satisfactory closed-loop system performance. The proposed discrete-time control scheme provides the engineers with a manner of direct and easier implementation via networked digital computers. It is shown that the bounded stability of the closed-loop system can be guaranteed. Finally, an application design example of a dc-dc buck converter with experimental results is conducted to illustrate the efficiency of the proposed control scheme.

It has been demonstrated in a large number of experimental results that small RNAs (sRNAs) play a vital role in gene regulation processes. Thus, the gene regulation process is dominated by sRNAs in addition to messenger RNAs and proteins. However, the regulation mechanism of sRNAs is not well understood and there are few models considering the effect of sRNAs. So it is of realistic biological background to include sRNAs when modeling gene networks. In this paper, sRNAs are incorporated into the process of gene expression and a new differential equation model is put forward to describe cyclic genetic regulatory networks with sRNAs and multiple delays. We mainly investigate the stability and bifurcation criteria for two cases: 1) positive cyclic genetic regulatory networks and 2) negative cyclic genetic regulatory networks. For a positive cyclic genetic regulatory network, it is revealed that there may exist more than one equilibrium and the multistability can appear. Sufficient conditions are established for the delay-independent stability and fold bifurcations. It is found that the dynamics of positive cyclic gene networks has no bearing on time delays, but depends on the biochemical parameters, the Hill coefficient and the equilibrium itself. For a negative cyclic genetic regulatory network, it is proved that there exists a unique equilibrium. Delay-dependent conditions for the stability are derived, and the existence of Hopf bifurcations is examined. Different from the delay-independent stability of positive gain networks, the stability of equilibrium is determined not only by the biochemical parameters, the Hill coefficient and the equilibrium itself, but also by the total delay. At last, three illustrative examples are provided to validate the major results.

In this paper, we aim to investigate the optimal synchronization problem for a group of generic linear systems with input saturation. To seek the optimal controller, Hamilton-Jacobi-Bellman (HJB) equations involving nonquadratic input energy terms in coupled forms are established. The solutions to these coupled HJB equations are further proven to be optimal and the induced controllers constitute interactive Nash equilibrium. Due to the difficulty to analytically solve HJB equations, especially in coupled forms, and the possible lack of model information of the systems, we apply the data-based off-policy reinforcement learning algorithm to learn the optimal control policies. A byproduct of this off-policy algorithm is shown that it is insensitive to probing noise that is exerted to the system to maintain persistence of excitation condition. In order to implement this off-policy algorithm, we employ actor and critic neural networks to approximate the controllers and the cost functions. Furthermore, the estimated control policies obtained by this presented implementation are proven to converge to the optimal ones under certain conditions. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

In this paper, the consensus problem is investigated for a class of nonaffine nonlinear multiagent systems (MASs) with actuator faults of partial loss of effectiveness fault and biased fault. To deal with the control difficulty caused by the nonaffine dynamics, a neural network (NN)-based adaptive consensus protocol is developed based on the Lyapunov analysis. The neuron input of the NN uses both the state information and the consensus error information. In addition, the negative feedback term of the NN weight update law is multiplied by an absolute value of the consensus error, which is helpful in improving the consensus accuracy. With the developed adaptive NN consensus protocol, semiglobal consensus with a bounded residual consensus error of the MAS is achieved, and the bounded NN weight matrix is guaranteed. Finally, simulation results show that the developed adaptive NN consensus protocol has advantages of fast convergence rate and good consensus accuracy and has the capability of rapid response with respect to the actuator faults.

For the sparse system estimation problem, the l0norm constraint normalized least mean square (l0-NLMS) can offer improved convergence performance than the standard normalized least mean square (NLMS) algorithm. However, in the l0-NLMS algorithm, both choices of the step-size and zero-attractor parameters involve the conflicting requirement of fast convergence rate and low steady-state error. In this paper, we propose the joint adaptive step-size and zero-attractor l0-NLMS (JASZ-lo-NLMS) algorithm to address this issue. The proposed algorithm can simultaneously estimate the optimal step-size and zero-attractor derived by minimizing the mean-square deviation (MSD) at each iteration. Simulations are conducted to demonstrate the efficiency of the proposed algorithm in different signal-to-noise ratio (SNR) and sparse channel environments.

In this paper, we analyze stability of incremental exponential stability for switched nonlinear systems with time delay. The continuous contraction theory is generalized to introduce a new type of switching laws. On this basis, the incremental exponential stability is established for both stable and unstable subsystems within the overall switched nonlinear systems with time delay. Computer simulations are presented to validate the theoretical findings.

This paper addresses the consensus tracking problem of multi-agent systems (MASs) with directed switching topologies based on the multiple Lyapunov functions (MLFs) approach. The special feature of Laplacian matrices for topology candidates is explored to construct a new class of MLFs for the tracking error systems of leader-following MASs with directed switching topologies. Then the average dwell time (ADT) method is utilized to establish a sufficient condition that guarantees consensus tracking in the closed-loop MASs. The efficiency of the derived theoretical results and the merits of the proposed MLFs are demonstrated by computer simulations.

This paper considers the event-based containment control problem for second-order multi-agent systems. A novel event-triggered containment control protocol is proposed so as to carry out intermittent examination of the event-triggering condition at sampling instants. One important feature of the designed protocol is that only the sampled position data are used with no utilization of velocity measurements. It is shown that the realization of containment control is guaranteed under a sufficient condition which depends upon the control gains, the sampling period, and the spectrum of the Laplacian matrix among the followers. The effectiveness of the proposed event-triggered containment control protocol is demonstrated by a simulation example.

In this paper, the problem of survival of all agents through cooperative interactions in generalized Verhulst-Lotka-Volterra systems is addressed. The considered model has the feature that the strength of cooperation among agents is proportional to the similarity level of the agents' sizes. For the case that every agent has an individual capacity, some sufficient conditions are derived to ensure that all agents in generalized Verhulst-Lotka-Volterra systems can interact cooperatively with each other for reaching survival. An illustrative example is provided to show the efficiency of the theoretical results.

In this paper, the problem of distributed estimation over adaptive networks is studied. A new diffusion normalized least mean-square (DifNLMS) algorithm is developed to tackle the considered problem. The main idea of the developed variable multi-step-size DifNLMS (VMSSDifNLMS) algorithm is to assign an individual time-varying step-size for each delivered signal in the weight adaptation step. As such, the developed algorithm is able to achieve a good tradeoff between fast convergence rate and low misadjustment. Numerical simulations are presented to show that the VMSSDifNLMS algorithm outperforms the conventional DifNLMS, VSSDifLMS, and DifLMS with optimal adaptive combination in terms of both convergence rate and steady-state error.

Wireless energy harvesting is an emerging technology in the Internet of Things (IoT). Plenty of recent literature focused on balancing the information transfer and energy harvesting at the same time. Different from these works, we jointly consider the remote state estimation and wireless energy harvesting in IoT, and introduce a new cost function which is the weighted difference between the remote state estimation error at the estimator side and the harvested energy at the energy receiver side. In our framework, the collection of communication channel states is known asa prioriknowledge and the real-time channel state is not known. We consider the scenario that the transmitter can send the observation data for remote estimation and deliver the power for energy harvesting concurrently. A robust transmission power split algorithm is provided to minimize the cost function for the worst-case channel state. In addition, another robust power switch algorithm is designed for the scenario that the transmitter can only decide to send the observation data or deliver the power for energy harvesting at any time slot. At last, simulation examples are provided to show the effectiveness of our proposed robust transmission power allocation policies.

A second-order sliding-mode (SOSM) control method is developed for the regulation problem of a dc-dc buck converter. By taking into account the model uncertainties and external disturbances in the mathematical model, a sliding variable with relative of degree 2 is first constructed. Then, a new SOSM controller is developed such that the output voltage will well track the desired reference voltage. Theoretical analysis shows that the resulting closed-loop system is globally finite-time stable, while similar SOSM control results only give the proof for finite-time convergence. The way on how to implement the proposed SOSM algorithm is also presented. The theoretical findings are verified by extensive simulations and experiments.

The cluster synchronization problem is investigated using intermittent pinning control for the interacting clusters of nonidentical nodes that may represent either general linear systems or nonlinear oscillators. These nodes communicate over general network topology, and the nodes from different clusters are governed by different self-dynamics. A unified convergence analysis is provided to analyze the synchronization via intermittent pinning controllers. It is observed that the nodes in different clusters synchronize to the given patterns if a directed spanning tree exists in the underlying topology of every extended cluster (which consists of the original cluster of nodes as well as their pinning node) and one algebraic condition holds. Structural conditions are then derived to guarantee such an algebraic condition. That is: 1) if the intracluster couplings are with sufficiently strong strength and the pinning controller is with sufficiently long execution time in every period, then the algebraic condition for general linear systems is warranted and 2) if every cluster is with the sufficiently strong intracluster coupling strength, then the pinning controller for nonlinear oscillators can have its execution time to be arbitrarily short. The lower bounds are explicitly derived both for these coupling strengths and the execution time of the pinning controller in every period. In addition, in regard to the above-mentioned structural conditions for nonlinear systems, an adaptive law is further introduced to adapt the intracluster coupling strength, such that the cluster synchronization for nonlinear systems is achieved.

Recently, an adaptive exponential trigonometric functional link neural network (AETFLN) architecture has been introduced to enhance the nonlinear processing capability of the trigonometric functional link neural network (TFLN). However, it suffers from slow convergence speed, heavy computational burden, and poor robustness to noise in nonlinear acoustic echo cancellation, especially in the double-talk scenario. To reduce its computational complexity and improve its robustness against impulsive noise, this paper develops a recursive adaptive sparse exponential TFLN (RASETFLN). Based on sparse representations of functional links, the robust proportionate adaptive algorithm is deduced from the robust cost function over the RASETFLN in impulsive noise environments. Theoretical analysis shows that the proposed RASETFLN is stable under certain conditions. Finally, computer simulations illustrate that the proposed RASETFLN achieves much improved performance over the AETFLN in several nonlinear scenarios in terms of convergence rate, steady-state error, and robustness against noise.

In this paper, the problem of fault tolerant tracking control for a linear time-invariant system subject to actuator faults and saturations is addressed. An auxiliary system is developed to ensure actuators behave within amplitude and rate limits under the influence of partial loss of control effectiveness. Based on the auxiliary system, a fault tolerant compensation controller is constructed to guarantee tracking errors to converge to a small region. Some relationships among tracking errors, command signals, actuator faults, amplitude and rate limits as well as controller parameters are comprehensively studied and explicitly illustrated with formulas. An example of rudder-roll damping control for a cruise keeping ship is included to illustrate the proposed procedures and their effectiveness.

In this paper, the filtering problem is investigated for a class of discrete-time Markov jump linear parameter varying systems with packet dropouts and channel noises in the network surroundings. The partial accessibility of system modes with respect to the designed filter is described by a hidden Markov model (HMM). A typical behavior characterization mechanism is proposed in the communication channel including data losses and additive noises, which occurs in a probabilistic way based on two mutually independent Bernoulli sequences. With the aid of a class of Lyapunov function subject to parameter-dependent and mode-dependent constraints, sufficient conditions ensuring the existence of HMM-based filters are obtained such that the filtering error system is stochastically stable with a guaranteed H∞error performance. The influence of monotonicity on the performance index is explored while changing the degree of both additive noise and mode inaccessibility. The effectiveness and applicability of the obtained results are finally verified by two numerical examples.

In this paper, the non-fragile finite-time tracking control problem is addressed for a class of uncertain linear systems with controller multiplicative coefficient variations. An adaptive control strategy is constructed to ensure that the system tracks a time-varying target orbit. The relationship of the bound of tracking errors and the size of uncertainties and controller multiplicative coefficient variations is deeply investigated. On the basis of Lyapunov stability theory, it shows that the bounded tracking of resulting adaptive system can be reached within a finite time, and the tracking errors of the system can be reduced as small as desired by adjusting controller parameters. The effectiveness of the proposed design is illustrated via a decoupled longitudinal model of F-18 aircraft.

Current control of IPMSM servo system is investigated in this paper. Under the field-weakening control framework, nonlinear couplings of the voltage equations are very strong. The current control performance may not be well until such nonlinear couplings are well dealed. In this case, a DOB-based model predictive control is proposed to improve the current control performance under field-weakening control framework. The proposed control method consists of a forecasted model based on disturbance observer, feedback correction for model mismatch rejection, and receding optimization for an optimized control input. Numerical simulations demonstrate that proposed method achieves nonlinear coupling attenuation ability and well promising field-weakening control performance.

In this paper, the problem of network-based leader-following consensus is addressed for linear multi-agent systems with input saturation. First, a network-based consensus protocol with input saturation constraints is introduced to accommodate some network-induced effects such as delay, data quantization and time-varying sampling interval. Next, the Lyapunov-Krasovskii method is utilized to show the exponential convergence of the leader-following error system to a bounded set region under a delay-dependent stability condition, together with estimation of the region of initial conditions. Then with leveraging this stability condition, the gain matrix of the network-based consensus controller is designed for linear multi-agent systems.

In this paper, the average mean square deviation (MSD) analysis of the normalized least mean square (NLMS) and least mean square (LMS) algorithms is carried out for cyclostationary input signals. It is shown that the NLMS algorithm has good transient response, while the steady-state MSD of the LMS algorithm does not depend on the periodic input power. In addition, the theoretical results reveals that for cyclostationary input signals, under small step-size conditions, the LMS algorithm can offer smaller steady-state average MSD than the NLMS algorithm at the same convergence rate. That is to say, the NLMS algorithm will suffer from large steady-state MSD for cyclostationary input signals. The theoretical results are validated by computer simulations.

This paper is dedicated to designing periodic impulsive control strategy for generating globally stable periodic solutions for periodic neural networks with discrete and unbounded distributed delays when such neural networks do not have stable periodic solutions. Two criteria for the existence of globally exponentially stable periodic solutions are developed. The first one can deal with the case where no bounds on the derivative of the discrete delay are given, while the second one is a refined version of the first one when the discrete delay is constant. Both stability criteria possess several adjustable parameters, which will increase the flexibility for designing impulsive control laws. In particular, choosing appropriate adjustable parameters can lead to partial state impulsive control laws for certain periodic neural networks. The proof techniques employed includes two aspects. In the first aspect, by choosing a weighted phase space PCα, a sufficient condition for the existence of a unique periodic solution is derived by virtue of the contraction mapping principle. In the second aspect, by choosing an impulse-time-dependent Lyapunov function/functional to capture the dynamical characteristics of the impulsively controlled neural networks, improved stability criteria for periodic solutions are attained. Three numerical examples are given to illustrate the efficiency of the proposed results.

This paper studies network-based practical leader-following consensus problem of heterogeneous multiagent systems with Lipschitz nonlinear dynamics under both fixed and switching topologies. Considering the effect of network-induced delay, a network-based leader-following consensus protocol with heterogeneous gain matrix is proposed for each follower agent. By employing Lyapunov-Krasovskii method, a sufficient condition for designing the network-based consensus controller gain is derived such that the leader-following consensus error exponentially converges to a bounded region under a fixed topology. Correspondingly, the proposed design approach is then extended to the case of switching topology. Two numerical examples with networked Chua's circuits are given to show the efficiency of the design method proposed in this paper.

In this paper, the stability analysis problem of Takagi-Sugeno fuzzy systems with time-varying delay is investigated. By utilizing the Wirtinger-based integral inequality and the improved reciprocally convex combination technique, an improved stability condition is derived in terms of linear matrix inequalities. A numerical example is given to demonstrate the efficiency of the obtained result.

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

The bipartite consensus problem for a group of homogeneous generic linear agents with input saturation under directed interaction topology is examined. It is established that if each agent is asymptotically null controllable with bounded controls and the interaction topology described by a signed digraph is structurally balanced and contains a spanning tree, then the semi-global bipartite consensus can be achieved for the linear multiagent system by a linear feedback controller with the control gain being designed via the low gain feedback technique. The convergence analysis of the proposed control strategy is performed by means of the Lyapunov method which can also specify the convergence rate. At last, the validity of the theoretical findings is demonstrated by two simulation examples.

In this paper, the state estimation problem for a class of discrete-time switched neural networks with modal persistent dwell time (MPDT) switching and mixed time delays is investigated. The considered switching law, not only generalizes the commonly studied dwell-time (DT) and average DT (ADT) switchings, but also further attaches mode-dependency to the persistent DT (PDT) switching that is shown to be more general. Multiple communication channels, which include one primary channel and multiredundant channels, are considered to coexist for the state estimation of underlying switched neural networks. The desired mode-dependent filters are designed such that the resulting filtering error system is exponentially mean-square stable with a guaranteed nonweighted generalized 112 performance index. It is verified that better filtering performance index can be achieved as the number of channels to be used increases. The potential and effectiveness of the developed theoretical results are demonstrated via a numerical example.

In this paper, the reachable set estimation problem is investigated for delayed discrete-time Takagi-Sugeno fuzzy systems with bounded input disturbances and nonzero initial conditions. A reachable set estimation condition is derived by using the reciprocally convex combination approach to bound the forward difference of triple-summable terms. The derived condition guarantees that all the states of the system with the initial conditions from some domain are bounded in a compact set, and all the states from other domain converge exponentially within another compact set. Moreover, a less conservative stability condition is also obtained. The effectiveness and the reduced conservatism of the proposed results are illustrated by numerical examples.

The containment control is studied for the second-order multiagent systems over a heterogeneous network where the position and velocity interactions are different. We consider three cases that multiple leaders are stationary, moving at the same constant speed, and moving at the same time-varying speed, and develop different containment control algorithms for each case. In particular, for the former two cases, we first propose the containment algorithms based on the well-established ones for the homogeneous network, for which the position interaction topology is required to be undirected. Then, we extend the results to the general setting with the directed position and velocity interaction topologies by developing a novel algorithm. For the last case with time-varying velocities, we introduce two algorithms to address the containment control problem under, respectively, the directed and undirected interaction topologies. For most cases, sufficient conditions with regard to the interaction topologies are derived for guaranteeing the containment behavior and, thus, are easy to verify. Finally, six simulation examples are presented to illustrate the validity of the theoretical findings.

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

In this paper, the contraction theory is used to analyze the synchronization for a collection of partial-state linearly coupled linear systems. First, the synchronization problem of the linear systems is transformed by defining proper error variables such that a stability problem of error systems is to be investigated. Then, the contraction analysis is performed with respect to the error system dynamics. It turns out that the error system dynamics is contracting, which in turn proves that the original systems reach synchronization exponentially fast. In addition, a brief comparison between Lyapunov method and contraction analysis is also provided. Finally, two examples are presented in order to illustrate the effectiveness of the theoretical result.

This paper is concerned with impulsive stabilization of periodic solutions of recurrent neural networks (RNNs) with discrete and distributed delays. By considering two different types of bounded discrete-delays, two stability criteria are formulated respectively for the case where the information on the discrete-delay derivative is unknown and the case where the discrete-delay derivative be strictly less than one. It is shown that the first stability criterion is an essential improvement over the existing one in the literature. When the discrete-delay is constant, the second stability criterion is proved to be less conservative than the first stability criterion. Moreover, impulsive control law design for delayed RNNs is facilitated by adjustable parameters in the stability criteria. The usefulness of the theoretical findings is demonstrated by a numerical example.

In this paper the stability problem for switched nonlinear systems with random disturbances of finite-order moments is investigated. The multiple Lyapunov functions method is utilized to establish certain general conditions under which random s itched systems are guaranteed to have a unique solution. Then the noise-to-state stability of random switched nonlinear systems is examined in two cases. First, the criterion of the noise-to-state stability of random switched systems with state-dependent min-switching is developed by the multiple Lyapunov functions method. Second, the criterion of the noise-to-state stability of random switched systems with time-dependent switching s derived in conjunction with the average dwell-time approach. The theoretical findings are verified by an illustrative example.

This paper introduces an efficient soft decision-directed algorithm (SDDA) for blind equalization of 4 quadrature amplitude modulation (4-QAM) systems. A novel cost function (CF) based on the conventional SDDA is established to efficiently obtain the weight vector of the blind equalizer (BE). An appropriately modified Newton method (MNM) which is proposed in our previous work [9] is adopted to fast search the optimal equalizer weight vector. It is shown that the BE based on the novel MNM has an approximately quadratic order of convergence like Newton methods. Moreover, the proposed algorithm has better stability and much lower computational load than Newton methods.

In this paper, the problem of stochastic input-to-state stability (SISS) is investigated for a class of stochastic switched nonlinear systems. A sufficient condition is first derived to estimate an upper bound on a stochastic process. Based on that, the SISS problem is addressed for stochastic nonlinear systems be virtue of the indefinite Lyapuno function approach. For the convenience of simulation, the SISS property of stochastic switched nonlinear systems i further analyzed by the average dwell-time technique. An illustrative example together with numerical simulatio s is presented to demonstrate the efficiency of the proposed results.

This paper studies the fault-tolerant consensus problem for a group of double-integrator agents with actuator faults and strongly connected topology. The proposed fault-tolerant consensus protocol is an active fault-tolerant control strategy which consists of a nominal control and an estimation of fault severity. To solve the fault-tolerant consensus problem, a Lyapuov method is employed based on the algebraic connectivity of strongly connected digraph. The results show that the consensus will be achieved if the nominal control is designed properly and the estimation of actuator fault is within a certain accuracy. Finally, a simulation example is given to demonstrate the validity of the theoretical results.

This paper addresses the problem of coexistence and dynamical behaviors of multiple equilibria for competitive neural networks. First, a general class of discontinuous nonmonotonic piecewise linear activation functions is introduced for competitive neural networks. Then based on the fixed point theorem and theory of strict diagonal dominance matrix, it is shown that under some conditions, such n -neuron competitive neural networks can have 5(n) equilibria, among which 3(n) equilibria are locally stable and the others are unstable. More importantly, it is revealed that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibria and locally stable equilibria than the ones with other activation functions, such as the continuous Mexican-hat-type activation function and discontinuous two-level activation function. Furthermore, the 3(n) locally stable equilibria given in this paper are located in not only saturated regions, but also unsaturated regions, which is different from the existing results on multistability of neural networks with multiple level activation functions. A simulation example is provided to illustrate and validate the theoretical findings.

This paper presents a novel non-Gaussian stochastic control framework for the problem of disturbance estimation and rejection by combining fuzzy identification technology with disturbance observer design. First, fuzzy logic models are used to approximate the output probability density functions (PDFs) of non-Gaussian processes such that the task of PDF shape control can be reduced to a fuzzy weight dynamics modeling and control problem. Next, Takagi-Sugeno fuzzy models with multiple disturbances are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input, in which a novel disturbance-observer-based PI-type fuzzy feedback controller is designed to ensure the system stability and convergence of the tracking error to zero. Meanwhile, the disturbance estimation and attenuation performance as well as the state constrained requirement can also be guaranteed. Moreover, the novel composite observer is constructed by augmenting the disturbance estimation into the full-state estimation. The satisfactory tracking performance and full-state observation effect can be achieved by the designed optimization algorithm. Finally, simulations for paper-making process are given to show the efficiency of the proposed approach.

This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functional combined with the use of a new type of integral inequality for treating the reaction-diffusion terms. The impulse-time-dependent feature of the proposed Lyapunov functional can capture more hybrid dynamical behaviors of the impulsive reaction-diffusion neural networks than the conventional impulse-time-independent Lyapunov functions/functionals, while the new integral inequality, which is derived from Wirtinger's inequality, overcomes the conservatism introduced by the integral inequality used in the previous results. Numerical examples demonstrate the effectiveness of the proposed method. Later, the developed impulsive synchronization method is applied to build a spatiotemporal chaotic cryptosystem that can transmit an encrypted image. The experimental results verify that the proposed image-encrypting cryptosystem has the advantages of large key space and high security against some traditional attacks.

This paper addresses the problems of synchronization and state estimation for a class of discrete-time hierarchical hybrid neural networks (NNs) with time-varying delays. The hierarchical hybrid feature consists of a higher level nondeterministic switching and a lower level stochastic switching. The latter is used to describe the NNs subject to Markovian modes transitions, whereas the former is of the average dwell-time switching regularity to model the supervisory orchestrating mechanism among these Markov jump NNs. The considered time delays are not only time-varying but also dependent on the mode of NNs on the lower layer in the hierarchical structure. Despite quantization and random data missing, the synchronized controllers and state estimators are designed such that the resulting error system is exponentially stable with an expected decay rate and has a prescribed H∞ disturbance attenuation level. Two numerical examples are provided to show the validity and potential of the developed results.

In this paper the problem of designing a second-order sliding mode controller for nonlinear systems with bounded uncertainties is addressed. The adding a power integrator technique is applied to develop a new second-order sliding mode control algorithm. It is shown that under the controller thus designed, the resulting closed-loop system can achieve the finite-time Lyapunov stability, which is superior to the similar results in the literature that can only give the finite-time convergence. The performance of the new second-order sliding mode control algorithm is demonstrated by an illustrative example.

This paper is concerned with stability analysis of multiple equilibria for recurrent neural networks. A new type of activation function, namely, discontinuous Mexican-hat-type activation function, is proposed for recurrent neural networks. Then with the aid of the fixed point theorem, some sufficient conditions for coexistent multiple equilibria are obtained to guarantee that such n-neuron recurrent neural networks can have at least 4nequilibria. In view of the theory of strict diagonal dominance matrix, further stability analysis reveals that 3nequilibria are locally exponentially stable. The new results considerably improve the existing multistability results in the literature.

In this paper the problem of control of continuous-time systems subject to input saturation and external disturbance is studied. The key idea is to construct a disturbance observer to enable the design of an efficient anti-disturbance controller. The new disturbance observer based controller can ensure asymptotical stability of the resulting closed-loop system. Moreover, an estimation of the domain of attraction is provided, and it can also be maximized by using an iterative algorithm. The theoretical findings are validated by a numerical example.

The problem of identification of autoregressive (AR) signals with noisy measurements is considered. A new algorithm is proposed to estimate the AR parameters. To cope with the effect of the measurement noise that causes a bias in the least-squares estimate of the AR parameters, an efficient procedure is developed for estimating the measurement noise variance. The proposed identification algorithm is implemented via the Newton iterative scheme and is able to produce better parameter estimates. A numerical example is presented to show the efficiency of the new identification algorithm for noisy AR signals.

In this paper, a new approach based on the iterative learning control (ILC) is proposed for finite-time tracking of coupled continuous networks. The proposed ILC tracking approach makes use of instantaneous sampled-data. By virtue of the Bellman-Gronwall Lemma, it is shown that the instantaneous sampled-data based ILC approach can achieve the tracking of coupled continuous networks in a finite-time interval. Computer simulations are presented to demonstrate the good finite-time tracking performance of the proposed ILC approach.

This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.

In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which unifies several performance measures, such as the H∞ performance, passivity, l2 - l∞ performance, and dissipativity. By introducing a triple-summable term in Lyapunov function, the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term and then the extended dissipativity criterion for discrete-time neural networks with time-varying delay is established. The derived condition guarantees not only the extended dissipativity but also the stability of the neural networks. Two numerical examples are given to demonstrate the reduced conservatism and effectiveness of the obtained results.

In this paper, we discuss the coexistence and dynamical behaviors of multiple equilibrium points for recurrent neural networks with a class of discontinuous nonmonotonic piecewise linear activation functions. It is proved that under some conditions, such n -neuron neural networks can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable, based on the contraction mapping theorem and the theory of strict diagonal dominance matrix. The investigation shows that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibrium points and more locally stable equilibrium points than the ones with continuous Mexican-hat-type activation function or discontinuous two-level activation functions. An illustrative example with computer simulations is presented to verify the theoretical analysis.

A unified approach to the analysis of synchronization for complex dynamical networks, i.e., networks of partial-state coupled linear systems and networks of full-state coupled nonlinear oscillators, is introduced. It is shown that the developed analysis can be used to describe the difference between the state of each node and the weighted sum of the states of those nodes playing the role of leaders in the networks, thus making it feasible to consider the error dynamics for the whole network system. Different from the other various methods given in the existing literature, the analysis employed in this paper is demonstrated successfully in not only providing the consistent convergence analysis with much simpler form, but also explicitly specifying the convergence rate.

In this paper, a fractional-order recurrent neural network is proposed and several topics related to the dynamics of such a network are investigated, such as the stability, Hopf bifurcations, and undamped oscillations. The stability domain of the trivial steady state is completely characterized with respect to network parameters and orders of the commensurate-order neural network. Based on the stability analysis, the critical values of the fractional order are identified, where Hopf bifurcations occur and a family of oscillations bifurcate from the trivial steady state. Then, the parametric range of undamped oscillations is also estimated and the frequency and amplitude of oscillations are determined analytically and numerically for such commensurate-order networks. Meanwhile, it is shown that the incommensurate-order neural network can also exhibit a Hopf bifurcation as the network parameter passes through a critical value which can be determined exactly. The frequency and amplitude of bifurcated oscillations are determined.

In this paper, the problem of energy-to-peak state estimation for a class of discrete-time Markov jump recurrent neural networks (RNNs) with randomly occurring nonlinearities (RONs) and time-varying delays is investigated. A practical phenomenon of nonsynchronous jumps between RNNs modes and desired mode-dependent filters is considered, and a nonstationary mode transition among the filters is used to model the nonsynchronous jumps to different degrees that are also mode dependent. The RONs are used to model a class of sector-like nonlinearities that occur in a probabilistic way according to a Bernoulli sequence. The time-varying delays are supposed to be mode dependent and unknown, but with known lower and upper bounds a priori. Sufficient conditions on the existence of the nonsynchronous filters are obtained such that the filtering error system is stochastically stable and achieves a prescribed energy-to-peak performance index. Further to the recent study on the class of nonsynchronous estimation problem, a monotonicity is observed in obtaining filtering performance index, while changing the degree of nonsynchronous jumps. A numerical example is presented to verify the theoretical findings.

This paper proposes a fast discrete-time learning algorithm for speech enhancement of single-channel noisy speech signal, based on a noise constrained least squares estimate. Unlike existing learning algorithms for the noise constrained estimate, the proposed discrete-time learning algorithm has a low complexity and fast speed. Simulation results show that the proposed discrete-time learning algorithm has a faster speed than the existing learning algorithms for speech enhancement. Moreover, the proposed discrete-time learning algorithm has a good performance in having a significant gain in SNR at colored noise.

In this paper a new design method is proposed for sliding mode control of a class of nonlinear second-order systems with input saturation. The main idea is to combine the conventional terminal sliding mode manifold with a saturation function for construction of a new nonsingular terminal sliding mode manifold. By virtue of the bound of the uncertainty, the constructed terminal sliding mode manifold is employed to design a saturated controller directly for the nonlinear system. It is shown that the designed saturated controller guarantees the finite-time convergence of the states of the resulting closed-loop system to zero.

This paper is concerned with the problem of distributed state estimation for a class of sensor networks characterized by the discrete-time dynamical systems. The discrete-time model with mixed time delays is used to express the target system. Outputs of the sensors are measured under randomly occurring saturations caused by physical restrictions of the sensors. By utilizing output measurements from each individual sensor and its neighboring sensors, we design distributed state estimators with a view to approximating the states of the target system in a distributed manner. Moreover, we show that the estimation error systems are globally asymptotically stable in the mean square, and also provide the explicit expressions of the distributed estimator gains.

This paper proposes a space-time semi-blind equalizer (ST-SBE) for dispersive multiple-input multiple-output (MIMO) communication systems that employ high throughput quadrature amplitude modulation (QAM) signals. A novel cost function (CF) that integrates multimodulus algorithm (MMA) with soft decision-directed (SDD) scheme is established to efficiently obtain the weight vector associated with the ST-SBE. In the ST-SBE, a very short training sequence is used to provide a rough initial least squares estimate of the weight vector. An efficient modified Newton method (MNM) for minimizing the established cost function is proposed to fast search the optimal weight vector. Very interestingly, we prove that the proposed MNM has the same quadratic order of convergence as Newton methods. In addition, the proposed MNM has much lower computational complexity than Newton methods. Simulation results are provided to demonstrate that the ST-SBE has better performances than the gradient-Newton (GN)-based concurrent constant modulus algorithm (CMA) with SDD scheme (GN-CMA+SDD).

This paper studies oscillatory dynamics of two-gene regulatory networks which are mediated by small RNAs (sRNAs) and subject to multiple delays. First, stability of the positive fixed point and the existence of the local Hopf bifurcation are examined for sRNAs-mediated two-gene regulatory networks. Then sufficient conditions for periodic oscillation are established for such networks with multiple delays. Computer simulations are presented to illustrate the proposed results.

This paper is concerned with the bifurcation problem of bidirectional associative memory (BAM) neural networks with two delays. Two delays play different roles in dynamical behaviors of BAM neural networks. The characteristic equation associated with delayed BAM neural networks is analyzed, which gives the distribution of the eigenvalues. Then some important dynamic properties of the trivial steady state, such as the local stability and the existence of Hopf bifurcation, are obtained. The theoretical results are further validated by numerical simulations.

The problem of stability of multiple equilibria is studied in this paper for two kinds of recurrent neural networks with time-varying delays and activation functions symmetrical with respect to the origin on the phase plane. Some sufficient conditions are obtained to ensure that two kinds of recurrent neural networks can have (2m + 1)nequilibrium points and (m + 1)nof them are locally exponentially stable. The derived conditions are valuable extensions to the existing results on stability of multiple equilibria for recurrent neural networks with time-varying delays in the literature.

This paper is concerned with the problem of mean-square exponential stability of uncertain neural networks with time-varying delay and stochastic perturbation. Both linear and nonlinear stochastic perturbations are considered. The main features of this paper are twofold: 1) Based on generalized Finsler lemma, some improved delay-dependent stability criteria are established, which are more efficient than the existing ones in terms of less conservatism and lower computational complexity; and 2) when the nonlinear stochastic perturbation acting on the system satisfies a class of Lipschitz linear growth conditions, the restrictive condition P < δI (or the similar ones) in the existing results can be relaxed under some assumptions. The usefulness of the proposed method is demonstrated by illustrative examples.

In this paper, the problem of exponential stability for Takagi-Sugeno (T-S) fuzzy delay systems with delayed impulses is addressed. By means of the time-dependent Lyapunov function method combined with Razumikhin technique, a sufficient condition expressed in terms of linear matrix inequalities (LMIs) is obtained for exponential stability of T-S fuzzy delay systems with delayed impulses. The derived stability condition depends both on the lower bound and the upper bound of impulsive intervals, which is robust with respect to small impulse input delays. By solving a set of LMIs, an impulsive state feedback controller can be easily constructed. Two numerical examples are discussed to illustrate the effectiveness of the theoretical findings.

Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

In this paper, we investigate multistability of two kinds of recurrent neural networks with time-varying delays and activation functions symmetrical about the origin on the phase plane. One kind of activation function is with zero slope at the origin on the phase plane, while the other is with nonzero slope at the origin on the phase plane. We derive sufficient conditions under which these two kinds of n-dimensional recurrent neural networks are guaranteed to have (2m+1)(n) equilibrium points, with (m+1)(n) of them being locally exponentially stable. These new conditions improve and extend the existing multistability results for recurrent neural networks. Finally, the validity and performance of the theoretical results are demonstrated through two numerical examples.

Complex networks have found widespread real-world applications. One of the key problems in research of complex networks is topology identification, which is concerned with deciding the interaction patterns from observed dynamical time series. This presents a very challenging problem, especially in the absence of the knowledge of nodal dynamics and in the presence of system noise. In this paper a simple and yet efficient approach is proposed for topology identification of complex networks in such challenging scenarios. The main idea behind the proposed approach is to use piecewise partial Granger causality, which measures the directed connections of nonlinear time series influenced by hidden variables. The effectiveness of the proposed approach in relation to network parameters is demonstrated by a commonly-used testing network.

In this paper, we introduce the fractional order into a three-node recurrent neural network model, and then consider the effect of the order on the system dynamics for the neural network based on a fractional-order differential equation. By applying the existing theorems on the stability of commensurate fractional-order systems, we investigate the linear stability and Hopf-type bifurcation for the fractional-order neural network model. Our analysis shows that the equilibrium point, which is unstable in the classic integer-order model, can become asymptotically stable in our fractional-order model, which is also confirmed by numerical simulations. Moreover, we also present simulation results of limit cycles produced by the fractional-order neural network model. It is shown that the amplitude of limit cycles increases with the order, while the frequency of limit cycles has robustness against the change in the order due to its small variation.

The problem of exponential stability of multiple equilibria in recurrent neural networks with time-varying delays and concave-convex characteristics is addressed in this paper. The focus is placed upon derivation of some sufficient conditions under which an neural network of order n can have (2k + 2m - 1)nequilibrium points with (k + m)nof them having local exponential stability. The new results represent important extensions of the existing results on multistability of delayed recurrent neural networks.

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.

There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.

In this paper, stability of multiple equilibria of neural networks with time-varying delays and concave-convex characteristics is formulated and studied. Some sufficient conditions are obtained to ensure that an n-neuron neural network with concave-convex characteristics can have a fixed point located in the appointed region. By means of an appropriate partition of the n-dimensional state space, when nonlinear activation functions of an n-neuron neural network are concave or convex in 2k+2m-1 intervals, this neural network can have (2k+2m-1)n equilibrium points. This result can be applied to the multiobjective optimal control and associative memory. In particular, several succinct criteria are given to ascertain multistability of cellular neural networks. These stability conditions are the improvement and extension of the existing stability results in the literature. A numerical example is given to illustrate the theoretical findings via computer simulations.

The problem of the convergence of the consensus strategies for multiple agents with double-integrator dynamics is studied in this paper. The investigation covers two kinds of different settings. In the setting with the interaction topologies for the position and velocity information flows being modeled by different graphs, some sufficient conditions on the fixed interaction topologies are derived for the agents to reach consensus. In the setting with the interaction topologies for the position and velocity information flows being modeled by the same graph, we systematically investigate the consensus algorithm for the agents under both fixed and dynamically changing directed interaction topologies. Specifically, for the fixed case, a necessary and sufficient condition on the interaction topology is established for the agents to reach (average) consensus under certain assumptions. For the dynamically changing case, some sufficient conditions are obtained for the agents to reach consensus, where the condition imposed on the dynamical topologies is shown to be more relaxed than that required in the existing literature. Finally, we demonstrate the usefulness of the theoretical findings through some numerical examples.

The problem of state estimation for Markovian jumping Hopfield neural networks (MJHNNs) with delays is addressed in this paper. It is assumed that sector- bounded conditions are obeyed by the neuron activation function and perturbed function of the measurement equation. An LMI (linear matrix inequality) based state estimator and a stability criterion for delay MJHNNs are developed. It is shown that the designed estimator ensures the mean-square exponential stability of the resulting error system. Moreover, the delay-dependent sufficient conditions are derived in a simple and effective manner. Numerical results are presented which show that the proposed method is very promising for state estimation of Hopfield neural networks.

This paper is concerned with synchronizing complex networks with arbitrary topological structures via pinning control. The necessary and sufficient conditions are established for choosing the pinned nodes to guarantee the pinning synchronzability of the complex networks. Under the assumption of the sufficient large coupling strength, it is shown that the way to pin the nodes is a decisive factor in determining the pinning synchronizability of complex networks and the entire network can achieve an exponentially fast speed of synchronization.

In this paper we consider the problem of designing reduced-order H2filters for discrete linear repetitive processes (LRPs). The design criterion is that a reduced-order filter thus designed must guarantee the filtering error process to be stable along the pass and meantime minimize an upper bound for the H2norm of its transfer function. The convex linearization approach is developed to fulfil the task of designing the desired reduced-order H2filters. The performance of the designed filter is demonstrated by numerical results.

The problem of blind separation of complex-valued signals via joint diagonalization of a set of non-unitary target matrices is addressed in this paper. An improved blind source separation (BSS) algorithm is developed based on minimization of the Frobenius-norm formulation of the approximate joint diagonalization problem by using a multiplicative update. Such minimization yields a strictly diagonally-dominant updated matrix at each iteration. With relaxing some constraints on the target matrices, the improved BSS algorithm allows for extended applications. The behavior of the improved BSS algorithm is demonstrated by computer simulation results in comparison with some representative BSS algorithms.

This brief focuses on the robust mean-square exponential stability and L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.

By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.

This paper is concerned with applying the impulsive control scheme to generalized synchronization (GS) of complex networks of nonlinear dynamical systems. The auxiliary-system approach is utilized to show that complex dynamical networks consisting of nonidentical systems can reach generalized synchronization under impulsive control. Then the relations between the GS error and the topological parameter are examined for scale-free networks, which reveals that an increase in the topological parameter causes a decrease in the GS error. Also, the relations between the GS speed and the adding of random edges are investigated for small-world networks, which shows that increasing the probability of adding random edges can accelerate GS. Moreover, the effect of node dynamics on the GS speed is studied for both small-world and scale-free networks.

This paper is concerned with analyzing mean square exponential stability of stochastic delayed neural networks subject to parametric uncertainties. The discretized Lyapunov functional technique is first utilized to construct a new Lyapunov functional in order to effectively deal with the time-varying delay. Then the free-weighting matrix technique and the convex combination method are used to establish a new delay-dependent mean square exponential stability criterion for uncertain stochastic delayed neural networks. The usefulness of the new theoretical findings is further demonstrated by numerical results.

This paper is concerned with blind separation of convolutive sources. The main idea is to make an explicit exploitation of block Toeplitz structure and block-inner diagonal structure in autocorrelation matrices of source signals at different time delays as well as of inherent relations among these matrices. With implementation of joint block diagonalization, a tri-quadratic cost function is introduced so that the mixture matrix can be extracted from a set of the correlation matrices of the observed vector sequence without pre-whitening. In this novel one-stage algorithm, every iteration step involves finding the closed solution to the corresponding least squares problem. Once the estimate of the mixing matrix is obtained, the source signals are retrieved by the classical least squares methods. The performance of the proposed algorithm is illustrated by simulation results.

The problem of designing robust ℋ∞filters for uncertain Markovian stochastic systems with time-varying delays is addressed in this paper. It is assumed that the Markovian systems are perturbed by Itô-type stochastic disturbances and subjected to parametric uncertainties and mode transition rate uncertainties. The main specification of robust ℋ∞filters under design is to ensure that the filtering error system is robustly stochastically stable and a prescribed ℋ∞disturbance attenuation level is met in the face of all admissible parameter uncertainties and time-delays. It is shown that the desired robust ℋ∞filters can be readily designed by solving some linear matrix inequalities which are derived by using a stochastic Lyapunov-Krasovskii functional and the free-weighting matrix technique.

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 brief investigates the problem of mean square exponential stability of uncertain stochastic delayed neural networks (DNNs) with time-varying delay. A novel Lyapunov functional is introduced with the idea of the discretized Lyapunov-Krasovskii functional (LKF) method. Then, a new delay-dependent mean square exponential stability criterion is derived by applying the free-weighting matrix technique and by equivalently eliminating time-varying delay through the idea of convex combination. Numerical examples illustrate the effectiveness of the proposed method and the improvement over some existing methods.

This paper presents new complete stability results for delayed cellular neural networks (DCNNs). A novel method is proposed for complete stability analysis of DCNNs. By applying the M-matrix theory and introducing some new estimation techniques on the solutions of DCNNs, a simple and improved complete stability criterion is derived. The new criterion unifies the delay-dependent and delay-independent complete stability conditions for DCNNs. Moreover, the obtained delay-dependent criterion can give a larger upper bound of the time delay than the existing ones such that the complete stability can still be retained. Numerical examples are presented which show that the new complete stability results for DCNNs are compared favorably with the existing results.

This paper is concerned with the problem of exponential stability analysis of continuous-time switched delayed neural networks. By using the average dwell time approach together with the piecewise Lyapunov function technique and by combining a novel Lyapunov-Krasovskii functional, which benefits from the delay partitioning method, with the free-weighting matrix technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with constant and time-varying delays, respectively. Moreover, the decay estimates are explicitly given. The results reported in this paper not only depend upon the delay but also depend upon the partitioning, which aims at reducing the conservatism. Numerical examples are presented to demonstrate the usefulness of the derived theoretical results.

In this brief, stability of multiple equilibria of recurrent neural networks with time-varying delays and the piecewise linear activation function is studied. A sufficient condition is obtained to ensure that n-neuron recurrent neural networks can have (4k - 1)(n) equilibrium points and (2k)(n) of them are locally exponentially stable. This condition improves and extends the existing stability results in the literature. Simulation results are also discussed in one illustrative example.

This paper addresses the problem of asymptotic stability for discrete-time recurrent neural networks with time-varying delay. The analysis starts with a general assumption that the time-varying delay may be expressed as the lower bound plus the length of an interval over which the delay varies. Then the delay partitioning technique is used to establish a new delay-dependent sufficient condition under which the asymptotic stability of recurrent neural networks with time-varying delay can be guaranteed. The new stability criterion takes the form of linear matrix inequalities, thus lending itself to being readily checkable by the available software package. The obtained theoretical result is further illustrated by numerical results, including their superiority over the existing results on asymptotic stability of delayed recurrent neural networks.

This paper is concerned with carrying out synchronization between two completely different complex dynamical networks, which is called generalized outer synchronization. Our objective is to develop a control scheme so as to achieve this generalized outer synchronization. The derived result is built upon Barbalat's Lemma. It is shown that when two complex dynamical networks to be synchronized have different topologies and diverse node dynamics, the designed controller is a nonlinear one. In particular, when two complex dynamical networks have the same topological structures or identical dynamics, the designed controller can be simplified, sometimes to a linear controller. Computer simulations are presented to show that generalized outer synchronization can be quickly realized under the designed controller.

This paper is concerned with robust passivity analysis of a class of uncertain nonlinear singular time-delay systems as well as with passivity-based sliding mode control of such systems. First we derive a delay-dependent sufficient condition expressed by means of linear matrix inequalities for achieving the generalized quadratic stability and robust passivity of the sliding mode dynamics. Then we show that the system's trajectories can be driven onto the pre-defined switching surface in a finite through synthesis of a sliding mode control law. Finally we present a numerical example that demonstrates the applicability of the derived theoretical results.

In this paper the problem of finite impulse response (FIR) filtering with noisy input-output data is investigated. A set of algebraic equations is derived for noisy FIR filtering. An analysis reveals that the derived set of algebraic equations provides a way for separating the estimation of the FIR filter parameters from that of the input noise variance that determines the noise-induced bias in the standard least-squares parameter estimates. This separation enables that the input noise variance is estimated by conducting optimizing search over an objective function. With this done, an estimate of the FIR filter parameters can be easily obtained without involving any iteration procedure. Numerical results are given to demonstrate the effectiveness of the proposed algorithm for noisy FIR filtering.

This paper addresses the L(2)- L(infinity) dynamic output feedback (DOF) control problem for a class of nonlinear fuzzy ItO stochastic systems with time-varying delay. The focus is placed upon the design of a fuzzy DOF controller guaranteeing a prescribed noise attenuation level in an L(2)- L(infinity) sense. By using the slack matrix approach, a delay-dependent sufficient condition is derived to assure the mean-square asymptotic stability with an L(2) - L(infinity) performance for the closed-loop system. The corresponding solvability condition for a desired L(2)- L(infinity) DOF controller is established. Since these obtained conditions are not all expressed in terms of linear matrix inequality (LMI), the cone complementary linearization method is exploited to cast them into sequential minimization problems subject to LMI constraints, which can be easily solved numerically. Finally, numerical results are presented to demonstrate the usefulness of the proposed theory.

The problem of global exponential stability analysis of impulsive neural networks with variable delays is investigated in this paper. The cases of impulses considered in this paper include that (1) the impulses are input disturbance; (2) the impulses are “neutral” type (that is, they are neither helpful for stability for neural networks nor destabilizing). A new approach based on Lyapunov function and Razumikhin-type techniques is developed to establish delay-independent sufficient conditions for global exponential stability in each case of impulses. These new stability conditions are expressed in form of linear matrix inequalities with regard to proper types of impulse time sequences and are independent of the size of variable delays. The effectiveness of the new results are further illustrated by numerical examples.

This paper studies the blind identification problem for multiple-input multiple-output (MIMO) FIR channels, in which the source signals are modulated by high order periodic precoders. A set of new design criteria of the periodic precoders is proposed from the channel blind identification viewpoint for multiuser channels. It is shown that, due to the fact that high order periodic precoders are used in the modelling problem, certain simple orthogonality among user’s signals can be introduced by a set of proper designed periodic precoders. This results in a simple blind identification algorithm and the impulse response of every subchannel can also be determined uniquely up to a unitary scalar based on the received signals’ second order statistics (SOS) and a priori information of the periodic precoders. Simulation results illustrate that the developed blind identification algorithm is efficient.

The problem of blind source separation (BSS) using joint diagonalization of a set of non-unitary eigen-matrices that are obtained with the observed signal vector sequence is addressed in this paper. A theoretical study is conducted of the identifiability of joint diagonalization of non-orthogonal matrices so as to generalize some known results for the orthogonal case. In particular, a mathematical proof is provided for essential uniqueness of general joint diagonalization, that is to say, all the estimated mixing matrices extracted from the non-unitary eigen-matrix group are essentially equal within an arbitrary permutation and scaling. The non-orthogonal identifiability theorem given in this paper serves as a mathematical foundation for the BSS methods based on the non-orthogonal joint diagonalization.

This paper is concerned with infinite impulse response (IIR) filtering where the measurements of both the input and the output of the filter are corrupted by noise. Making use of the ratio of the variances of the input noise and the output noise which decide the estimation bias, a least-squares (LS) based method is developed for unbiased IIR filtering. One algorithmic feature is that the proposed method is fully based upon the standard LS method in the sense that no evaluation of the average LS errors and other covariances is required. Then the sensitivity of the proposed LS based method with respect to the ratio of the input and output noise variances is studied. The sensitivity analysis reveals that the assumption of the given noise variance ratio can be relaxed to that of a rough range of this ratio, thereby broadening the application domain of the proposed method for noisy IIR filtering. The theoretical findings are corroborated with numerical results.

This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lyapunov-Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz-Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for asymptotic stability of delayed neural networks (DNNs). A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing stability criteria.

Adaptive IIR filtering in the case where noise exists in both the input and output of the system amounts to solving over-determined normal equations. In this paper a recursive total instrumental-variable (RTIV) algorithm is proposed for tracking the total least-squares (TLS) solution of the normal equations in the over-determined instrumental-variable methods. It is shown that the weight vector in the RTIV algorithm converges to the direction parallel to the singular vector associated with the smallest singular value of the augmented cross-correlation matrix. Moreover, the estimated parameters of the adaptive IIR filter are optimal in the TLS sense and its noise rejection capability is superior to that of the least-squares based algorithms. The appealing behavior of the RTIV algorithm for noisy adaptive IIR filtering is substantiated by simulation results.

This paper is concerned with FIR filtering with noise-corrupted input-output measurements. With an analysis of the algebraic structure of the correlation matrix, it is shown that an unbiased estimate of FIR parameters can be obtained by solving a special bilinear equation. Then a bilinear equation method (BEM) is developed for solving the bilinear equation associated with the unbiased solution of the FIR filtering under the unknown ratio of the input noise variance to the output noise variance (NNR). Being different from the existing unbiased estimators, the main advantage is that the proposed method exploits much sufficiently the special structure of the correlation matrix and obtains much accurate estimation for FIR filtering in the presence of input and output noises. Simulation results are presented to validate the good performance of the proposed method.

The problem of unbiased estimation of autoregressive (AR) signals subject to colored noise is investigated. The previously proposed improved least-squares method for colored noise (called ILS-CN) is revisited. This leads to derivation of a new system of bilinear equations with respect to the AR parameters and the colored observation noise autocovariances. It is shown that separate estimations of the AR parameters and the autocovariance vector of the colored observation noise can be made by using the separable least-squares method to solve the derived system of bilinear equations. The new estimation method is superior to the previous ILS-CN method in that there is no need to alternate estimations between the AR parameters and the colored observation noise autocovariances; and it can enhance the accuracy of the AR parameter estimates by forming an overdetermined system of bilinear equations. Computer simulations verify the theoretical predictions.

The problem of stabilization of discrete-time delayed systems is investigated in this paper. Based on Lyapunov-Krasovaii functional approach, a delay-dependent stability condition is derived in terms of linear matrix inequalities (LMIs), for the discrete-time delayed systems. In the derivation of the delay-dependent stability result, the model transformation and bounding certain cross terms are avoided. Although the stability condition is given in terms of LMIs, it is found to be not suitable for control design. As a remedy for this, the stability condition is converted to the equivalent linear matrix inequalities with inverse constraints (ICLMIs) which can be employed in designing controllers. Based on the ICLMIs condition, a new delay-dependent stabilization condition for discrete-time delayed systems is given in terms of ICLMIs, which is tractable numerically by the cone complementarity linearization algorithm. Finally, a numerical example and the comparison with an LMI-based result are given to demonstrate the applicability and the less conservativeness of the proposed approach respectively

In this paper, new approaches to H∞ controller design for a class of discrete-time nonlinear fuzzy systems are proposed based on a relaxed approach in which basis-dependent Lyapunov functions are used. First, two relaxed conditions of nonquadratic stability with H∞ norm bound are presented for this class of systems. The two relaxed conditions are shown to be useful in designing fuzzy control systems. By introducing some additional instrumental matrix variables, the two relaxed conditions are used to develop H∞ controllers. In the control design, the first relaxed condition has fewer inequality constraints, but only admits a common additional matrix variable while the second one can admit multiple additional matrix variables. Finally, two examples are given to demonstrate the applicability of the proposed approach. © 2007 IEEE.

This paper addresses the problem of complete stability analysis for cellular neural networks (CNNs) with variable delays. The M-matrix theory and new analysis techniques are utilized to establish novel delay-independent/delay-dependent sufficient conditions under which the complete stability of CNN's with variable delays can be guaranteed. The new stability criteria do not require any symmetric condition of the feedback matrix and the delayed feedback matrix, thus being much widely applicable. The developed theoretical results are further illustrated by numerical examples, including their superiority over the existing results.

The problems of existence, uniqueness and global exponential stability of the equilibrium of Cohen-Grossberg neural networks with time-varying delays are investigated in this paper. A new approach is developed to establish delay-independent/dependent sufficient conditions for global exponential stability. The results obtained can be easily checked in practice and do not require the delays to be constant or differentiate. In particular, our delay-dependent exponential stability conditions give explicitly the allowable upper bound of the delays that guarantees stability of Cohen-Grossberg neural networks, and are applicable to the case when the non-delayed terms cannot dominate the delayed terms. The effectiveness of the new results are further illustrated by numerical examples in comparison with the existing results.

In this paper, we develop a fast recursive algorithm with a view to finding the total least squares (TLS) solution for adaptive FIR filtering with input and output noises. We introduce an approximate inverse power iteration in combination with Galerkin method so that the TLS solution can be updated adaptively at a lower computational cost. We further reduce the computational complexity of the developed algorithm by making efficient computation of the fast gain vector. We then make a careful investigation into global convergence of the developed algorithm. Simulation results are provided that clearly illustrate appealing performances of the developed algorithm.

In this paper an improved whitening scheme is first developed by estimating the signal subspace jointly from a set of diagonalization-structural matrices based on the proposed cyclic maximizer of an interesting cost function. Next, a biquadratic contrast function is proposed for extracting one single independent component from a slice matrix group of any order cumulant of the array signals in the presence of the spatially-temporally white noise. A fast fixed-point algorithm is constructed for searching a minimum point of the proposed contrast function. Then multiple independent components are obtained by using repeatedly the fixed point algorithm for extracting one single independent component, and the orthogonality among them is achieved by the well-known QR decomposition. The performance of the proposed algorithms is illustrated by simulation results and is compared with several representative blind source separation algorithms.

Recently, a combined transmitter antenna selection/receiver-maximal-ratio-combining (TAS/MRC) scheme has been proposed to reduce the complexity of system and retain the diversity advantage. In this paper, we investigate the performance of the TAS/MRC scheme over correlated Nakagami fading channels. The characteristics function method is used to derive an exact bit-error rate expression, in which only very simple functions are included. Two special cases are discussed. The theoretical findings are supported by computer simulations.

This paper is concerned with parameter estimation of autoregressive (AR) signals from noisy observations. A set of bilinear equations has been derived for noisy AR signal estimation. An analysis reveals that the derived set of bilinear equations can be efficiently solved by using the separable least-squares method. That is, estimation of the observation noise variance can be conducted separately from that of the AR parameters. Once the observation noise variance has been estimated, an estimate of the AR parameters can be easily obtained without involving any iteration procedure. It is also shown that the estimate of the observation noise variance can be improved by using an overdetermined set of bilinear equations. Numerical results are given to demonstrate the effectiveness of the proposed estimation algorithm.

A novel random-gradient-based algorithm is developed for online tracking the minor component (MC) associated with the smallest eigenvalue of the autocorrelation matrix of the input vector sequence. The five available learning algorithms for tracking one MC are extended to those for tracking multiple MCs or the minor subspace (MS). In order to overcome the dynamical divergence properties of some available random-gradient-based algorithms, we propose a modification of the Oja-type algorithms, called OJAm, which can work satisfactorily. The averaging differential equation and the energy function associated with the OJAm are given. It is shown that the averaging differential equation will globally asymptotically converge to an invariance set. The corresponding energy or Lyapunov functions exhibit a unique global minimum attained if and only if its state matrices span the MS of the autocorrelation matrix of a vector data stream. The other stationary points are saddle (unstable) points. The globally convergence of OJAm is also studied. The OJAm provides an efficient online learning for tracking the MS. It can track an orthonormal basis of the MS while the other five available algorithms cannot track any orthonormal basis of the MS. The performances of the relative algorithms are shown via computer simulations.

A new least-squares-based method is established to perform unbiased parameter estimation of linear systems using noisy input and output measurements. The significance of the developed method lies in its improved computational efficiency since the underlying noisy system is now identified in a direct manner, with the augmented noisy system being introduced only as an auxiliary system but not actually being identified. Simulation results confirm that the presented efficient implementation scheme can retain the same estimation accuracy with reduced numerical costs.

A fast convergent algorithm for unbiased identification of noisy autoregressive (AR) signals is presented. This algorithm is developed based on a bias correction procedure, but makes use of more autocovariances to estimate the variance of the corrupting noise which determines the noise-induced bias in the least-squares estimates of the AR parameters. Since better estimates of this corrupting noise variance can be attained at earlier stages of the iterative process, the proposed algorithm can achieve a faster rate of convergence. Simulation results are included that illustrate the good performances of the proposed algorithm.

This paper addresses the problem of parameter estimation of linear systems with noisy input-output measurements. A new and simple estimation scheme for the variances of the white input and output measurement noises is presented which is based on expanding the denominator polynomial of the system transfer function only and makes no use of the average least-squares (LS) errors. The attractive feature of the iterative LS based parametric algorithm thus developed is its improved convergence property. The effectiveness of the developed identification algorithm is demonstrated through numerical illustrations.

An efficient method is proposed for identification of linear noisy input-output systems. Central to this method is that the variances of the input and output noises, which determine the bias in the ordinary least-squares (LS) estimator, are estimated in the way of increasing the degrees of both the denominator and the numerator of the system transfer function by one, but with no need to evaluate the average LS errors. While achieving estimation unbiasedness, the proposed method exhibits algorithmic advantages over the LS-based algorithms recently developed. Performance comparisons with other existing estimation algorithms based upon computer simulations are given.

Autoregressive (AR) modeling has played an important role in many signal processing applications. This paper is concerned with the identification of AR model parameters using observations corrupted with colored noise. A novel formulation of an auxiliary least-squares estimator is introduced so that the autocovariance functions of the colored observation noise can be estimated in a straightforward manner. With this, the colored noise-induced estimation bias can be removed to yield the unbiased estimate of the AR parameters. The performance of the proposed unbiased estimation algorithm is illustrated by simulation results. The presented work greatly extends the author's previous method that was developed for identification of AR signals observed in white noise.

This paper presents new recursive procedures for designing optimum envelope-constrained (EC) filters. Using a constraint transcription technique, the inequality constrained quadratic programming problem associated with EC filtering can be approximated as an unconstrained minimization problem. Two types of optimization methods are developed to solve this unconstrained problem in a recursive adjusting manner. Simulations of the proposed recursive procedures are presented which support the theoretical predictions.

The problem of envelope-constrained filters with uncertain input (ECUI) was first formulated as a nonsmooth optimization problem. Previously, it was shown that this nonsmooth problem is equivalent to a standard quadratic programming problem which can be solved efficiently. The objective of the present paper is to investigate several issues relating to the sensitivity of ECUI filters. They are: (i) the output mask tolerance; (ii) the effect of the location of the pulse peak in the output response on the output mask tolerance; and (iii) the effect of the order of ECUI filters on the output mask tolerance. Theoretical analysis is supported by numerical simulation studies.>

This paper is concerned with the maximum robustness design of envelope-constrained filters with uncertain input (ECUI). It is shown that the maximum robustness ECUI filter design can be formulated as a minimax optimization problem in which the set of feasible filters is characterized by nonsmooth matrix inequalities. On the basis of the use of a newly developed transformation technique and the use of standard linear programming and quadratic programming, an efficient design algorithm is developed to solve this problem. The attractive feature of the proposed algorithm is that the ECUI filter thus designed can allow for the greatest uncertainty in the input signal while still achieving the acceptable filter performance. A numerical example is given to illustrate the effectiveness of the algorithm.>

Signal processing is used to obtain unbiased parameter estimates for stochastic systems subject to correlated disturbances. A filter is designed and inserted artificially into the identified system so that the resulting system has some known zeros which can be used to estimate the bias arising from the correlated disturbances. It is shown that the proposed algorithm gives a consistent estimate and that the application of signal processing to system identification is very heuristic.>