In dynamic systems, complex time-varying noise characteristics impose challenges for multi-sensor data fusion. This paper proposes a deep learning-driven noise-adaptive filtering approach for multi-sensor data fusion. A parallel gated recurrent unit (GRU) and one-dimensional convolutional neural network (1D-CNN) architecture jointly extracts temporal dependencies and local spatial patterns from raw measurement sequences to estimate sensor noise variances. Each sensor then uses these variance estimates for more accurate adaptive local filtering. Based on these refined local estimates, the fusion center employs an event-triggered scheme to drastically cut communications while preserving fusion accuracy. Comprehensive experiments in a high-fidelity Unreal Engine–AirSim quadrotor unmanned aerial vehicle (UAV) simulation under nominal, drift, abrupt, and extreme noise modes confirm that our pipeline achieves superior global state estimation while dramatically reducing data transmission burden.

This article investigates the chaotic suppression problem of a chaotic system with uncertain parameters. To solve this problem, a dynamic surface constructed based on the adaptive backstepping control method is considered. First, a novel dynamic surface is introduced in the controller design process, which is utilized to alleviate the "complexity explosion" problem of classical backstepping. Then, piecewise functions are used to avoid the singularity of virtual controllers and real controllers, and the proposed control scheme can achieve state stabilization of chaotic systems based on the fixed-time theory. The results indicate that the chaotic state can converge to a small neighborhood near the origin. Finally, simulations are used to verify the validity of the proposed approach, and the control scheme is applied to a permanent magnet synchronous motor (PMSM) and suppresses its chaotic behavior.

A fan-powered tailsitter is capable of operating in both rotary-wing and fixed-wing flight modes. The transition between these modes is critical due to strong disturbances and considerable control complexity. This article investigates a predefined-time stability tracking control problem for tailsitters subject to parameter uncertainties and external disturbances. Based on the second-order dynamic model and high-order fully actuated (HOFA) system approach, a high-order robust controller is first developed to address the limitations of existing mode transitions, particularly in terms of control accuracy and disturbance suppression capabilities. On this basis, a novel predefined-time HOFA scheme is proposed by introducing adjustable parameters, which enables the system states to converge into a small neighborhood of the desired equilibrium within a prescribed time, while providing flexible tuning of the convergence time to adapt to varying mission and environmental requirements. Theoretical analysis and numerical simulations demonstrate that the proposed scheme achieves enhanced control accuracy, faster convergence, and improved robustness compared with conventional approaches. In contrast to existing approaches, the proposed HOFA-based predefined-time framework allows explicit tuning of the convergence time and provides robustness guarantees under parameter uncertainties, an aspect that has not been sufficiently addressed in the current literature.

The rapid development of deep learning-based image inpainting poses serious challenges to image authenticity. As inpainting methods continue to evolve, the inpainted images exhibit extremely high visual fidelity, presenting recognition difficulties to the forgery detection model due to differences in operational mode and forgery traces among methods. In particular, the detection performance tends to drop significantly in the testing phase when the test samples differ from the training data. To address this issue, we propose a test-time adaptive detection framework for image inpainting forgeries. First, we propose an image gradient-based metric that quantifies model uncertainty and orchestrates the entire adaptation process. Integrating this metric with sample-specific batch normalization (BN) statistics enhances the ability of pretrained models in the inference stage. Second, we introduce a cross-attention module as a side-tuning module, enabling the model to adapt dynamically to reliable test samples without altering the backbone network. To validate the effectiveness of the proposed method, we construct a dataset comprising synthetic images of multiple inpainting methods and design experiments under two scenarios of distributional bias. The results demonstrate that our proposed framework outperforms the existing baseline method, enhancing the adaptability and detection performance of the forgery detection model in dynamic environments.

This study investigates the extended dissipativity analysis for uncertain delayed genetic regulatory networks (GRNs) within an interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy framework. To the best of our knowledge, this is the first attempt to capture the complex dynamics and parameter uncertainties of GRNs via IT2 fuzzy sets, providing a robust representation through lower and upper membership functions. By constructing a novel Lyapunov-Krasovskii (L-K) functional, explicit derivations of the maximum admissible delay bounds are obtained. A unified analytical framework is established to verify ${\mathcal {L}}_{2}$ - ${\mathcal {L}}_{\infty }$ performance, $H_{\infty }$ attenuation, passivity, and dissipativity. To further reduce conservatism, a membership-function-dependent (MFD) stability criterion is developed that fully exploits the characteristics of IT2 fuzzy sets. Numerical examples demonstrate that the proposed approach significantly outperforms existing methods in terms of less conservative stability conditions and improved dissipativity indices under uncertainties and delays. This work advances theoretical understanding and practical design of robust control strategies for GRNs, with potential applications in synthetic biology.

Ground-to-air communication is a critical technology for establishing an Internet of Things (IoT) network system, especially in emergency situations. We are investigating the trajectory planning problem of a data collection IoT network assisted by an unmanned aerial vehicle (UAV). This article aims to solve the data collection Dubins traveling salesman problem (DCDTSP) for UAVs in a three-dimensional and complex obstacle environment. To optimize the paths for UAVs in data collection from terminals to UAVs, a novel releasing-collecting-recycling (RCR) framework has been established for heterogeneous multi-UAVs. In the UAV release step, we propose a multi-height hierarchical target clustering (MHTC) algorithm to enhance the efficiency of multi-target clustering. In the data collection step, a bundling ant colony system (BACS) is developed to minimize the length of the obstacle avoidance path while still meeting the communication throughput constraint. Meanwhile, the dynamic adaptive window probabilistic roadmap (DAWPRM) algorithm has been enhanced to address the obstacle avoidance distance in BACS. In the UAV recycling step, we propose a time synchronous Dubins recycling strategy to plan the simultaneous arrival trajectory for multiple UAVs with a constrained turning radius. The results of simulation experiments showed that the proposed RCR framework is optimal for finding Pareto solutions for DCDTSP.

DC microgrids (DCMG) enable efficient integration of renewable energy systems (RES) and energy storage systems (ESS). However, stability under constant power loads and disturbances remains challenging. Existing studies often overlook how control strategies concretely improve stability. This paper focuses on the dc/dc boost converter within the ESS and investigates how the introduction of a disturbance observer can improve overall system performance. A comprehensive model of the DCMG, including the RES, ESS, and electric vehicle (EV) loads, is established. Control strategies and observer design are presented, followed by a stability analysis using the minor loop gain methods. Simulation results validate the theoretical analysis and demonstrate improved dynamic response and robustness of the overall system.

Dynamics-based control offers a promising approach to exploring the motion potential of soft robots. However, inherently infinite degrees of freedom of these systems pose significant challenges for dynamics modeling, closely followed by the pressing robustness concerns arising from finite-dimensional approximations. This paper addresses these issues by proposing a physics-informed dynamics learning neural network and an adaptive fractional-order control for continuum soft robots. Specifically, a deep Lagrangian neural network is first developed with an embedded self-attention mechanism to enhance learning efficiency, accuracy, and data sensitivity. Subsequently, an adaptive fractional-order sliding mode controller is designed, leveraging the inherent historical memory properties of fractional calculus. This controller not only ensures robust shape control but also improves response speed and tracking accuracy. To further handle model discrepancies in the learned dynamics and external disturbances, a nonlinear disturbance observer is introduced to effectively estimate and compensate for lumped uncertainties, thereby ensuring reliable performance. Theoretical analysis confirms the closed-loop stability, while both simulation and experiment results validate the high dynamics fitting accuracy of the proposed network, as well as the robust and precise tracking capability of the fractional-order controller. Note to Practitioners—Soft robots offer great potential in unstructured or constrained environments owing to their compliance and adaptability. However, their high degrees of freedom and nonlinear behaviors make analytical modeling and robust control particularly challenging. Meanwhile, traditional closed-box learning methods often suffer from limited physical interpretability, reliability and extrapolability. This work presents a physics-informed dynamics learning framework combined with a fractional-order controller for soft robots. The dynamics learning network embeds physical priors to enhance model interpretability and extrapolability, while a self-attention mechanism improves data efficiency and modeling accuracy. Additionally, a disturbance observer is designed to estimate and compensate for model discrepancies and external disturbances, thereby contributing to the system’s robustness. Incorporating the observer’s outputs, the adaptive fractional-order controller further enhances closed-loop behavior by leveraging the memory properties of fractional calculus.

Teleoperated multi-robots rely on communications, both between the human’s local robot and the multiple remote robots and among the remote robots themselves, to execute the remote tasks commanded by their human operator. If attacked, the communications may lead to task failure, loss of multi-robot connectivity and possibly unstable teleoperation. To render teleoperated multi-robots resilient to Denial of Service (DoS) attacks with arbitrary frequency and duration, this paper augments a passivity-based controller for normal teleoperation with: 1) a controller that stops the remote robots at safe distances from each other and from obstacles when a DoS attack starts; and 2) a controller that restores the multi-robot connectivity before resuming normal teleoperation when a DoS attack stops. The teleoperation of a simulated multi-robot system with one leader and two followers illustrates the effectiveness of the proposed distributed control strategy.Note to Practitioners—Research and industry increasingly seek to use robots in inaccessible unstructured environments Teleoperated multi-robots are ideally suited for such environments because they fuse human cognition with remote multi-robotic execution. However, they can be hindered by cyber attacks on their communications. As cyber attacks become more prevalent, resilience to them becomes increasingly important for practical teleoperated multi-robots. This paper presents a first distributed control strategy for rendering teleoperated multi-robots resilient to DoS attacks. For industrial practitioners, the proposed strategy has two key advantages: 1) it is straightforward to implement; and 2) it is effective for DoS attacks with arbitrary frequency and duration. Future work will tackle teleoperated multi-robots under malicious attacks.

This paper is focused on the event-based secure control issue for cyber-physical systems (CPSs) under aperiodic denial-of-service (DoS) attacks. Malicious DoS attacks disrupt the communication between the controller and the actuator. The finite attack resources of malicious attackers are taken into consideration, and the DoS attacks are characterized using an aperiodic model. In contrast to prior results, the present study tackles the issue of secure controller design by considering the attributes of the DoS attack, instead of employing a switched system approach to address the aforementioned concerns. More specifically, under aperiodic DoS attacks, sufficient criteria are established to guarantee that the closed-loop CPSs can achieve bounded stability. Then, within a time-varying attack period, the relationship between the attack active interval and the attack silent interval is derived. Without satisfying the derived conditions, the system’s stability will deteriorate. Moreover, an event-based secure control scheme under aperiodic DoS attacks is designed. To verify the efficacy of the derived theory, a wheeled mobile robot system under aperiodic DoS attacks is illustrated. Note to Practitioners—CPSs have been widely utilized in various domains, such as aerospace and intelligent transportation. However, the openness of networks provides attackers with numerous opportunities for malicious assaults, consequently leading to a degradation in system performance. Consequently, researching the security issues of CPSs under malicious attacks is of utmost urgency. This paper focuses on the issue of event-triggered secure control for CPSs in the presence of energy-constrained aperiodic DoS attacks. The event-triggered communication mechanism is introduced to reduce the computational burden. The criteria for ensuring the bounded stability of CPSs under aperiodic DoS attacks are proposed. The relationship between the attack active interval and the attack silent interval is derived, which is incorporated into the proposed criteria. A wheeled mobile robot system is given to validate the effectiveness of the proposed method. In the future, an active defense control method will be proposed to counter malicious attacks.

In this paper, a generalized proportional integral observer (GPIO)-based finite-time sliding mode control scheme is proposed to enhance the convergence rate and disturbance rejection capacity of the grid-connected three-level neutral-point-clamped (NPC) converter. Firstly, a generalized super-twisting algorithm (GSTA) is designed in the voltage regulation loop and the instantaneous power loop of the NPC converter. Compared with the conventional super-twisting algorithm (STA), this method provides a faster convergence speed while the system trajectories are far from the origin, which is applied to the NPC converter to improve its dynamic performance and robustness. Additionally, by introducing a generalized proportional integral observer (GPIO) combined with the GSTA in the voltage regulation loop, the unknown time-varying disturbances can be rejected effectively. The stability of the proposed control scheme is proved. Finally, a set of comparative experiments between the proposed controller and the classic STA-based method have been carried out based on a three-level NPC converter prototype, and the results confirmed the efficacy of the proposed control strategy.

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods.

This article focuses on the issue of time-delay unknown input actuator fault detection (FD) observer design dedicated to the discrete-time Takagi–Sugeno fuzzy singular system. Based on the given$L_{\infty }$performance and finite-frequency$H_{-}$indexes, the observer is established to construct the residuals that are robust against disturbances and sensitive to actuator faults simultaneously. Under the assumption that disturbances are unknown but bounded, according to the set-membership techniques, all FD thresholds generated by fault-free residuals are propagated in a sequence of zonotopes. Moreover, compared with existing unknown input observers, the designed observer overcomes the issue of incomplete decoupling between residuals and unknown disturbances, thereby reducing the conservatism in FD. Furthermore, by introducing arbitrary matrices and relaxation matrices, the originally nonconvex observer design conditions are transformed into a minimization optimization problem under linear matrix inequalities. The tradeoff between robustness to disturbances and sensitivity to faults can be achieved by the iterative solution algorithm, contributing to less conservatism and satisfactory FD performance. The effectiveness of the proposed FD strategy is validated by simulations based on a truck–trailer dynamic model.

In this article, we discuss the event-based secure estimation issue for 2-D cyber-physical systems subject to deception attacks in the sensor-to-estimator channel. The game-theoretic framework is applied to established the equilibrium defense policy against the malicious attacks, and meanwhile the dynamic event-triggered mechanism is proposed for the limited communication resource. By resorting to Lyapunov functional approach and matrix techniques, sufficient conditions are attained to assure that the considered state estimation error dynamic is asymptotically mean square stable with an$H_\infty$performance. Then, based on the zero-sum game theory, a valid mixed defense mechanism is proposed. A defense-based remote estimator design algorithm that considers the interaction of the physical layer and the cyber layer is established. Finally, the validity of the developed estimation scheme is certificated by a simulation example.

This article explores a novel variable-gain super-twisting observer (VGSTO)-based fixed-time sliding mode control (FTSMC) method for the dc-link voltage control of three-level neutral-point-clamped (NPC) active front-end (AFE) converters. Among the literature review, the super-twisting observer is an attractive solution for disturbance estimation. However, its convergence velocity is limited when the trajectories are far away from the origin. To overcome this drawback, a novel VGSTO is proposed in this work, whose innovation lies in adopting dynamically adapted exponent coefficients in the conventional super-twisting observer; thus, it turns into a linear observer outside a ball around the origin, which increases the convergence rate of conventional super-twisting observer, and the disturbance rejection ability is enhanced significantly. In addition, an FTSMC is designed for the NPC converter and its settling time is independent of the initial states, which breaks through the limitation of the traditional finite-time sliding mode controllers and assures the dynamic performance of the NPC converter. The proposed control scheme is assessed and compared with other representative observer-based sliding mode control methods based on a three-level NPC AFE converter test bench, and the experimental results verify its feasibility and effectiveness.

This study focuses on the problem of multiunmanned aerial vehicle cooperative pursuit in complex obstacle environments and proposes a lightweight cooperative pursuit method with safety guarantees. By incorporating obstacle motion dynamics, an enhanced control barrier function constraint framework is constructed, effectively overcoming the limitations of traditional approaches that consider only geometric collision avoidance in dynamic environments. To enable the simultaneous pursuit of multiple evading targets, a hybrid task allocation strategy is designed without requiring complex optimization solvers. Building upon this strategy, a distributed quadratic optimization problem is formulated, aiming to minimize control input variations under safety constraints. A neurodynamic approach is employed to solve this optimization problem in real time, thereby achieving efficient and safe control during the cooperative pursuit process. Subsequently, the stability and safety of the closed-loop system are theoretically analyzed. Finally, extensive simulations and physical experiments demonstrate the effectiveness and practicality of the proposed method in multitarget cooperative pursuit tasks.

This article addresses the issue of security in cyber–physical systems (CPSs) in the context of malicious actuator false data injection (FDI) attacks. Building on a stochastic physical dynamics model, the proposed approach distinguishes itself by employing a moving target defense (MTD) strategy to enable proactive protection, which is an aspect rarely addressed in existing related works. The system model is formulated as a family of controllable submodels based on controllability. These controllable submodels are regarded as moving targets. A residual-based attack detector is introduced to justify whether an attack occurs or not. When an alarm is triggered, the current running controllable submodel is switched to another controllable one. Furthermore, an MTD-based security controller is devised to proactively mitigate actuator attacks. Sufficient conditions for the design of security control gains are formulated, using which the CPSs can preserve the mean-square exponential stability with the desired disturbance rejection level. Finally, the effectiveness of the proposed control strategy is demonstrated through a comparative simulation based on a practical physical system.

Advanced life forms, sustained by the synergistic interaction of neural cognitive mechanisms, continually acquire and transfer knowledge throughout their lifespan. In contrast, contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless, the emergence of large language models (LLMs) presents promising avenues for realizing CL via interactions with these models. Drawing on Complementary Learning System theory, this paper presents a novel Interactive Continual Learning (ICL) framework, enabled by collaborative interactions among models of various sizes. Specifically, we assign the ViT model as System1 and multimodal LLM as System2. To enable the memory module to deduce tasks from class information and enhance Set2Set retrieval, we propose the Class-Knowledge-Task Multi-Head Attention (CKT-MHA). Additionally, to improve memory retrieval in System1 through enhanced geometric representation, we introduce the CL-vMF mechanism, based on the von Mises-Fisher (vMF) distribution. Mean-while, we introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI) strategy to identify hard examples, thus enhancing collaboration between System1 and System2 for complex reasoning realization. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods. Code is available at github.com/ICL.

To overcome the heterogeneous dynamics and unreliable communication which adversely effect the stable control for connected and automated vehicle platoons, a new intelligent control approach, named virtual order-degradation interconnection and damping assignment (VO-IDA), is proposed in this article. First, the internal stability of vehicle platoons is abstracted into a class of tracking control problems for general chained integral systems. By converting the chained integral system into standard closed-loop port-controlled Hamiltonian form, VO-IDA achieves asymptotic tracking through the integration of backstepping order degradation and virtual stabilization control techniques. This conversion effectively eliminates the dependence on preceding vehicular acceleration as well. Second, under heterogeneous dynamics, explicit stable domains of control parameters are provided to ensure the attenuation of string stability for vehicle platoons via Laplace transform. Furthermore, a linear-proportional relationship between heterogeneous and homogeneous dynamics regarding spacing error ratio is uncovered. Leveraging this relationship, a modified multiobjective genetic algorithm is employed to online explore target locations within stable domains, enabling VO-IDA to conduct stable and precise control under heterogeneous dynamics. Comparative experiments verify the superiority of this approach.

From the perspective of information bottleneck (IB) theory, we propose a novel framework for performing black-box transferable adversarial attacks named IBTA, which leverages advancements in invariant features. Intuitively, diminishing the reliance of adversarial perturbations on the original data, under equivalent attack performance constraints, encourages a greater reliance on invariant features that contributes most to classification, thereby enhancing the transferability of adversarial attacks. Building on this motivation, we redefine the optimization of transferable attacks using a novel theoretical framework that centers around IB. Specifically, to overcome the challenge of unoptimizable mutual information, we propose a simple and efficient mutual information lower bound (MILB) for approximating computation. Moreover, to quantitatively evaluate mutual information, we utilize the Mutual Information Neural Estimator (MINE) to perform a thorough analysis. Our experiments on the ImageNet dataset well demonstrate the efficiency and scalability of IBTA and derived MILB. Our code is available at github.com/IBTA.

In multi-vehicle systems, the coupling problem between the task allocation and path planning and the variability of task execution solutions creates challenges for utility estimation and affects the effectiveness of distributed mission planning. To characterize the effect of task sequences on the task utilities and implement a task-extended distributed allocation, we propose a task-extended utility tensor algorithm (TEUTA) based on market mechanism. In the mission planning problem of multi-vehicle system, we consider the impact of the task schedule on the vehicle trajectory, and indicate the vehicle task execution utilities under different preceding task points in the form of tensors. Further, a task-extended utility tensor iterative algorithm (TEUTIA) is presented based on an iterative strategy to improve the algorithm in terms of computational complexity. A task execution utility estimation model and an algorithm framework are designed for the implementation of the two proposed algorithms. The simulation and experimental results show that compared with the non-tensor method, TEUTA and TEUTIA can achieve higher task execution performance, and TEUTIA has better computational efficiency. Note to Practitioners—This work presents two novel multi-vehicle distributed mission planning algorithms based on the market mechanism. TEUTA and TEUTIA proposed in this paper can be applied to address the impact of vehicle motion constraints on mission planning, which are widely present in various types of common nonholonomic vehicles such as two-wheel differential drive vehicles and Ackerman steering vehicles. In the application of the algorithms, an accurate kinematic model of the vehicles is required for trajectory planning to estimate the task execution reward precisely, which is necessary for effective mission planning. When the vehicle trajectory planning algorithm is computationally intensive, TEUTIA can significantly reduce the computational consumption and improve the mission planning efficiency compared with TEUTA without losing task execution reward. Finally, a stable inter-vehicle communication network is required for the interactive process of the market mechanism, where bi-directional communication exists between any two vehicles, to ensure the stability of mission planning.

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A straight-line flight judgment (SFJ) was developed to reduce the calculation of obstacle avoidance. An improved adaptive window probabilistic roadmap (AWPRM) algorithm is addressed to plan obstacle-avoidance paths. The AWPRM improves the feasibility of finding the optimal sequence based on the proposed SFJ compared with a traditional probabilistic roadmap. To solve the solution to TSP with obstacles constraints, the proposed sequencing-bundling-bridging (SBB) framework combines the bundling ant colony system (BACS) and homotopic AWPRM. An obstacle-avoidance optimal curved path is constructed with a turning radius constraint based on the Dubins method and followed up by solving the TSP sequence. The results of simulation experiments indicated that the proposed strategies can provide a set of feasible solutions for HMDTSPs in a complex obstacle environment.

In this article, a novel adaptive fixed-time fuzzy control algorithm is designed for uncertain Euler–Lagrange (EL) systems with actuator control input saturation. In contrast to existing algorithms, this article explores a faster fixed-time backstepping control algorithm. It enables the system to achieve fixed-time convergence with a faster convergence rate and obtain a smaller upper bound of the convergence time. To address the problem of actuator control input saturation, a novel fixed-time auxiliary system is constructed, involving coordinate transformation of the system's error variables to mitigate the effects of saturation. In response to the unknown dynamics (including model uncertainty, external disturbance, etc.) of the EL system, this article designs an adaptive interval type-2 fuzzy neural network for estimation and compensation. Stability analysis confirms that the tracking error can achieve faster fixed-time convergence. Simulation and experimental results demonstrate that the proposed control algorithm can enhance dynamic and steady-state tracking control performance.

Load frequency control (LFC) is widely employed in power systems to stabilize frequency fluctuation and guarantee power quality. However, most existing LFC methods rely on accurate power system modeling and usually ignore the nonlinear characteristics of the system, limiting controllers' performance. To solve these problems, this article proposes a model-free LFC method for nonlinear power systems based on deep deterministic policy gradient framework. The proposed method establishes an emulator network to emulate power system dynamics. After defining the action-value function, the emulator network is applied for control actions evaluation instead of the critic network. Then, the actor network controller is effectively optimized by estimating the policy gradient based on zeroth-order optimization and backpropagation algorithm. Simulation results and corresponding comparisons demonstrate the designed controller can generate appropriate control actions and has strong adaptability for nonlinear power systems.

In this article, on the basis of an existing dual layer nested (DLN) adaptive sliding-mode control (ASMC) strategy, an observer-based sliding-mode control strategy with an improved DLN adaptive tuning mechanism (IDLN-ASMC) is proposed for a three-level neutral-point-clamped converter. The proposed controller not only ensures good system performance but also mitigates two problems of the existing DLN-ASMC strategy. Meanwhile, three objectives are achieved. First, an adaptive supertwisting algorithm is utilized in the power tracking loop to converge power tracking errors to bounded regions in finite time. Next, a disturbance observer-based IDLN-ASMC strategy is proposed in the dc-link voltage regulation loop to regulate the dc-link voltage to its reference value. Finally, a simple proportional-integral controller is used in the dc-link voltage-balancing loop to reduce the voltage difference between the two dc-link capacitors. The results of simulation and experiment demonstrate the effectiveness and superiority of the proposed strategy.

In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.

This work is dedicated to adaptive decentralized tracking control for a class of strong interconnected nonlinear systems with asymmetric constraints. Currently, there are few related studies on unknown strongly interconnected nonlinear systems with asymmetric time-varying constraints. To deal with the assumptions of the interconnection terms in the design process, which include upper functions and structural restrictions, the properties of Gaussian function in radial basis function (RBF) neural networks are applied to overcome this difficulty. By constructing the nonlinear state-dependent function (NSDF) and using a new coordinate transformation, the conservative step that the original state constraint converts into a new boundary of the tracking error is removed. Meanwhile, the virtual controller's feasibility condition is removed. It is proven that all the signals are bounded, especially the original tracking error and the new tracking error, which are both bounded. In the end, simulation studies are carried out to verify the effectiveness and benefits of the proposed control scheme.

Deep neural networks (DNNs)-based natural language processing (NLP) systems are vulnerable to being fooled by adversarial examples presented in recent studies. Intent detection tasks in dialog systems are no exception, however, relatively few works have been attempted on the defense side. The combination of linear classifier and softmax is widely used in most defense methods for other NLP tasks. Unfortunately, it does not encourage the model to learn well-separated feature representations. Thus, it is easy to induce adversarial examples. In this article, we propose a simple, yet efficient defense method from the geometric constraint perspective. Specifically, we first propose an M-similarity metric to shrink variances of intraclass features. Intuitively, better geometric conditions of feature space can bring lower misclassification probability (MP). Therefore, we derive the optimal geometric constraints of anchors within each category from the overall MP (OMP) with theoretical guarantees. Due to the nonconvex characteristic of the optimal geometric condition, it is hard to satisfy the traditional optimization process. To this end, we regard such geometric constraints as manifold optimization processes in the Stiefel manifold, thus naturally avoiding the above challenges. Experimental results demonstrate that our method can significantly improve robustness compared with baselines, while retaining the excellent performance on normal examples.

This article examines the problems of task allocation and path optimization for multiobjective coverage in a congestion risk environment with obstacle constraints. An improved probabilistic roadmap (PRM*) algorithm is proposed, which eliminates the zig-zag paths around the path endpoints. The$K$-distance PRM*$(K$-DPRM*) provides a novel clustering metric for task allocation in an obstacle environment. An ant colony system-PRM* (ACS-PRM*) algorithm is proposed to solve the congestion avoidance traveling salesman problem (CATSP) by voyage optimization of multiobjective coverage. Additionally, the mapping relationship between the probability of environmental congestion and the velocity of robot is established and combined with the feedforward control method to improve the motion control of robots. Simulations and experiments verify the effectiveness of the path optimization method in obstacle environments with congestion risk.

Distributed cyber–physical systems (CPSs) are with complex and interconnected framework to receive, process, and transmit data. However, they may suffer from adversarial false data injection attacks due to the more open attribute of their cyber layers, and the connections with neighbor agents could aggravate the disastrous consequences on the system performance degradation. In this article, we focus on investigating distributed secure estimation paradigms against sparse actuator and sensor corruptions by virtue of combinational optimization. First, the consensus-based static batch optimization and secure observer design problems are established, based on which the concepts of sparsity repairability and restricted eigenvalues under attacks are discussed. Then, both the distributed projected heavy-ball estimator and distributed projected Luenberger-like observer are designed, in terms of the intensified combinational vote locations and distributed implementation of projection operator, with strict convergence guarantees. Finally, two numerical examples are performed to verify the effectiveness of our theoretical derivation.

Copy-move forgery can be used for hiding certain objects or duplicating meaningful objects in images. Although copy-move forgery detection has been studied extensively in recent years, it is still a challenging task to distinguish between the source and the target regions in copy-move forgery images. In this paper, a convolutional neural network-transformer based generative adversarial network (CNN-T GAN) is proposed to distinguish the source and target regions in a copy-move forged image. A generator is first utilized to generate a mask that is similar to the groundtruth mask. Then, a discriminator is trained to discriminate the true image pairs from the false ones. When the discriminator cannot discriminate the true/false image pairs accurately, the generator can be used to obtain the final localization maps of copy-move forgery. In the generator, convolutional neural network (CNN) and transformer are exploited to extract the local features and global representations in copy-move forgery images, respectively. In addition, feature coupling layers are designed to integrate the features in CNN branch and transformer branch in an interactive way. Finally, a new Pearson correlation layer is introduced to match the similarity features in source and target regions, which can improve the performance of copy-move forgery localization, especially the localization performance on source regions. To the best of our knowledge, this is the first work to utilize transformer for feature extraction in copy-move forgery localization. The proposed method can not only detect the copy-move regions, but also distinguish the source and target regions. Extensive experimental results on several commonly used copy-move datasets have shown that the proposed method outperforms the state-of-the-art methods for copy-move detection.

In this work, the problem of tracking control for discrete-time nonlinear actuator-saturated systems via interval type-2 (IT2) T–S fuzzy framework is investigated. Improved on the (type-1) T–S fuzzy system, the IT2 T–S fuzzy system has a better capability for the expression of system uncertainty, and correspondingly, it will increase the difficulty of analysis, especially for the membership-functions-dependent (MFD) method. In addition, in this case, the control input nonlinearity caused by actuator saturation will complicate the stability analysis of the systems. We make an attempt to address the challenges that the information of membership functions (MFs) is underutilized or not utilized, by developing an MFD analysis approach, which allows the enhancement of design flexibility of IT2 fuzzy controller and effectiveness of lessening the conservativeness of the analysis result. The piecewise MFs which are formed by connecting the sample point on or close to the original IT2 MFs are utilized to approximate the original IT2 MFs, and the error between the piecewise MFs and the original upper and lower MFs is taken into account in the stability analysis. To acquire the linear matrix inequality-based (LMI-based) constraint, the actuator saturation is converted to a sector nonlinear issue.$\mathcal {H}_{\infty }$performance is considered to limit the difference between the reference system and the control saturated system. Examples are presented to illustrate the validity of the results.

The security of spread spectrum (SS) watermarking largely depends on the difficulty of estimating its secret key. Some estimators have been proposed to estimate the secret key in the known-message attack (KMA) scenario. However, the estimation accuracies of existing estimators are not satisfactory when the number of observations is not large enough. Currently, it is still a challenging and open problem to design more effective estimators. In this paper, we propose an equivalent keys (EK)-based estimator to estimate the secret key for both the traditional and more secure SS watermarking methods. Equivalent keys form an equivalent region, which is the intersection of a unit hypersphere and a hypercone. According to the Monte Carlo simulation, we find that the secret key can be estimated by adding up the equivalent keys uniformly sampled from the equivalent region. Thus, the proposed estimator selects equivalent keys from randomly-generated vectors by exploiting the pairs of watermarked signals and their embedded messages. A theoretical analysis is performed for the proposed estimator to evaluate the estimation accuracy. Experimental results verify the theoretical analysis and show the superiority of the proposed estimator over existing estimation methods. Furthermore, this paper also shows the insecurity of the more secure SS watermarking methods in the KMA scenario from a practical perspective for the first time.

The problem of learning-based control for robots has been extensively studied, whereas the security issue under malicious adversaries has not been paid much attention to. Malicious adversaries can invade intelligent devices and communication networks used in robots, causing incidents, achieving illegal objectives, and even injuring people. This article first investigates the problems of optimal false data injection attack scheduling and countermeasure design for car-like robots in the framework of deep reinforcement learning. Using a state-of-the-art deep reinforcement learning approach, an optimal false data injection attack scheme is proposed to deteriorate the tracking performance of a robot, guaranteeing the tradeoff between the attack efficiency and the limited attack energy. Then, an optimal tracking control strategy is learned to mitigate attacks and recover the tracking performance. More importantly, a theoretical stability guarantee of a robot using the learning-based secure control scheme is achieved. Both simulated and real-world experiments are conducted to show the effectiveness of the proposed schemes.

The issue of event-based asymptotic tracking control for constrained multi-input–multi-output (MIMO) nonlinear systems by designing a single-parameter adaptation method is addressed in this work. Different from some works that rely on prior knowledge of the gain function, this work allows for unknown gain functions. This harsh condition is handled by introducing a new affine variable. Meanwhile, to eliminate the step of turning state constraints into new bounds for tracking errors, a new barrier function is provided instead of continuing to use the Log- or Tan-type barrier Lyapunov function (BLF). This imposes constraints on the system states instead of tracking errors, removing the strict feasibility conditions for virtual controllers. To reduce the computational burden, only one adaptive parameter is designed in this work. Then, an event-triggered strategy is integrated into the control scheme to reduce the waste of communication resources. The systems output can asymptotically track the desired trajectory even if the state of the closed-loop system meets the constraints. Simulation results confirm the validity of this scheme.

This article focuses on the problem of the observer-based sliding-mode control (SMC) for polynomial fuzzy singular systems via a novel dynamic event-triggered (DET) mechanism. First, the DET mechanism is introduced to reduce the transmission burden in the communication network. Second, the DET-based observer is constructed to estimate the unavailable state variables. On this basis, the integral sliding variable is provided, and the observer dynamics can be obtained. A sufficient condition is then proposed to make sure that the whole closed-loop system composed of the observer dynamics and the estimation error system is admissible and passive by utilizing the sum-of-squares approach. Meanwhile, the sliding surface parameter matrix, the observer gain, and the trigger weighting matrix are co-designed. It should be emphasized that the common equality constraints, nonstrict and nonlinear inequality constraints are removed through equivalent sets and several inequality techniques in this design process. Moreover, the DET observer-based SMC law is derived to assure the reachability of the specified sliding surface. Finally, the theoretical results are verified by simulation results.

This paper is concerned with the controller design for grid-connected inverter facing parameter variation and stochastic perturbation. Considering these two factors, a stochastically perturbed linear parameter varying (LPV) system for the inverter is formulated, upon which the stability analysis and controller synthesis have been conducted. Parameter dependent sufficient conditions have been obtained to guarantee the asymptotical and exponential mean square stability, with which the asymptotical and exponential controllers are designed respectively. A two-level three-phase inverter is used to verify the effectiveness of proposed theories. Simulation results show that both the controllers are effective under parameter variation and stochastic perturbation, and the exponential controller performs better than the asymptotical controller.

In this paper, a disturbance observer based control strategy is proposed to regulate the dc-link voltage of three-phase two-level pulse-width-modulation active-front-end rectifiers. In the voltage regulation loop, a linear disturbance observer is designed to improve the system performance. The load connected to the dc-link capacitor is considered as an external disturbance and the observer is used to estimate its value. The estimated disturbance is compensated to the PI controller. Simulations are carried out to verify the effectiveness and advantage of the proposed control strategy. Three different loads have been provided to test the robustness of the control strategy. Simulation results show that the proposed control strategy improves the transient response of the dc-link voltage, meanwhile maintains the steady-state performance in term of the output current total harmonic distortion, and has strong robustness against the external load variation.

Reversible data hiding (RDH) for color images has attracted increasing attention in recent years. Due to its effective utilization of the correlation between prediction errors, high-dimensional prediction-error expansion (PEE) can achieve much better performance for color image RDH than low-dimensional PEE. However, existing studies only focus on high-dimensional PEE with nonadaptive embedding. To further improve the embedding performance for color images, we propose a novel three-dimensional PEE method that is adaptive to image content. Double deep Q-network (DDQN), introduced to RDH for the first time, is adopted to find the optimal mapping paths for PEE. In addition, an action selection scheme is presented for DDQN to efficiently find the reversible mapping paths. Extensive experiments show that the proposed method outperforms existing color image RDH methods in image quality.

Image splicing can be easily used for illegal activities such as falsifying propaganda for political purposes and reporting false news, which may result in negative impacts on society. Hence, it is highly required to detect spliced images and localize the spliced regions. In this work, we propose a multi-task squeeze and excitation network (SE-Network) for splicing localization. The proposed network consists of two streams, namely label mask stream and edge-guided stream, both of which adopt convolutional encoder-decoder architecture. The information from the edge-guided stream is transmitted to the label mask stream for enhancing the discrimination of features between the spliced and host regions. This work has three main contributions. First, image edges, along with label masks and mask edges, are exploited to supply more comprehensive supervision for the localization of spliced regions. Second, the low-level feature maps extracted from shallow layers are fused with the high-level feature maps from deep layers to provide more reliable feature for splicing localization. Finally, several squeeze and excitation attention modules are incorporated into the network to recalibrate the fused features to enhance the feature expression. Extensive experiments show that the proposed multi-task SE-Network outperforms existing splicing localization methods evidently on two synthetic splicing datasets and four benchmark splicing datasets.

In this article, a novel distributed fuzzy load frequency control (LFC) approach is investigated for multiarea power systems under cross-layer attacks. The nonlinear factors existing in turbine dynamics and governor dynamics as well as the uncertain parameters therein are modeled and analyzed under the interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy framework. The cross-layer attacks threatening the stability of power systems are considered and modeled as an independent Bernoulli process, including denial-of-service (DoS) attacks in the cyber layer and phasor measurement unit (PMU) attacks in the physical layer. By using the Lyapunov theory, an area-dependent Lyapunov function is proposed and the sufficient conditions guaranteeing the system’s asymptotically stability with the area control error (ACE) signals satisfying$\mathcal {H}_{\infty }$performance are deduced. In simulations, we adopt a four-area power system to verify the resiliency enhancement of the presented distributed fuzzy control strategy against random cross-layer DoS attacks. Results show that the designed resilient controller can effectively regulate the load frequency under different cross-layer DoS attack probabilities.

To handle the common existing constraints, that is, limited energy supplies and limited communication bandwidth in multiagent systems (MASs), this article investigates the consensus problem in MASs with event-triggered communication (ETC) and state quantization. In order to compensate for the effect brought by mismatched disturbances, we also propose a novel multiple discontinuous sliding-mode surface, and the corresponding sliding-mode control law is constructed by considering the event-triggered and dynamic quantized mechanisms jointly. Under such a scheme, it is shown that the state trajectories of all the agents will be regulated to achieve consensus asymptotically and the Zeno behavior can be avoided completely. We further extend this work to self-triggered and periodic event-triggered cases. Particularly, in a periodic event-triggered approach, the new form of triggering conditions and upper bound of the sampling periods are provided explicitly. As a result, all agents can reach bounded consensus. Moreover, the upper bound of the consensus error can be arbitrarily adjusted by appropriately selecting parameters, and the periodic event-triggered case will be reduced to the event-triggered case when the bound approaches 0 (sampling periods approach 0 at the same time). A numerical example is illustrated to verify the effectiveness of the proposed algorithms.

dynamic output-feedback (DOF) controller for interval type-2 (IT2) T-S fuzzy systems with state delay. For nonlinear systems, the IT2 fuzzy model is an efficient modeling method which can better express uncertainties than the (type-1) fuzzy model. In addition, state delay is also a general factor that affects system performance. After analyzing the stability of the system, based on convex linearization and the projection theorem, this article proposes a delay-dependent output-feedback controller design method. The IT2 membership functions (MFs) of the fuzzy controller are chosen to be different from those of the model so as to increase the freedom of controller selection. A membership-function-dependent (MFD) method based on the staircase MFs is applied to relax the stability analysis results. Finally, a numerical simulation example is given to illustrate the effectiveness of the results.

The system nonlinearity, sensor saturation, and the uncertainty will hamper the analysis and affect the control performance. Filtering is a signal processing method which facilitates the system analysis and synthesis by signal estimation or noise suppression. To achieve generalized filtering problem for nonlinear systems with sensor saturations with lower computational burden, this article addresses the reduced-order extended dissipative filter design for nonlinear sensor-saturated system which is modeled by interval type-2 (IT2) T–S fuzzy system. For IT2 T–S fuzzy systems, the main challenge exists in the acquisition of the information in IT2 membership functions (MFs) for analysis and design. A membership-function-dependent (MFD) method is applied to capture the information of the MFs for reducing the conservativeness introduced by MFs not involved in the analysis. An extended dissipative filtering method, under imperfect premise matching (IPM) concept that the membership functions of the filter are different from those of the model, is proposed for sensor-saturated IT2 fuzzy systems. The proposed method has a high flexibility in parameters adjustment of both the filter and the design condition, including extended dissipative matrices, approximation MFs, and sensor saturation degree, one can freely choose the parameters according to the required performance of the fuzzy filter. A numerical example is given to demonstrate the effectiveness of the results.

This paper examines a trajectory homotopy optimization framework for multiple unmanned aerial vehicles (multi-UAV) to solve the problem of dynamic penetration mission planning (PMP) with hostile obstacles and perception constraints. Constrained problems are usually more challenging and difficult to solve with some practical constraints and requirements. To improve the efficiency of the solution for the penetration path, a novel variable-time mechanism has been constructed to adapt to the updated delay time of unknown target search (UTS) and dynamic trajectory planning (DTP) two stages. The occupancy grid maps are established by a Gaussian probability field (GPF) for predicting the positions of enemy UAVs. To fully consider the hostile obstacle constraint, a hybrid adaptive obstacle avoidance approach dynamic window PRM (DW-PRM) is designed to shorten the planned path. The penetration strategy algorithm (SG) is developed based on the proposed strategy set and decision tree. To improve the ability of dynamic obstacle avoidance, the multiple coupled penetration homotopy trajectory is addressed with a turning radius constraint. The simulation results indicated that the penetration homotopy framework for multi-constraints can solve the multi-UAV PMP problem.

This article proposes a sliding-mode-observer (SMO)-based control strategy to regulate the dc-link voltage for a three-phase two-level active front end (AFE). The SMO is designed for the voltage control loop to estimate the external load which is abruptly connected to the AFE dc-link and consequently causes the dc-link voltage fluctuation. The estimated load value is used to compensate the voltage loop controller, therefore, the voltage loop gains more robustness against the load perturbation and its disturbing effect is greatly reduced. The effectiveness and advantage of the proposed control strategy has been verified through theoretical analysis, simulations, and the real-application experiments conducted on a 5 KVA laboratory AFE. The results show that the proposed control strategy provides obvious improvement of the dc-link voltage control performance comparing with the conventional PI controller and demonstrates stronger robustness against the operating point variations caused by the changes in external load and dc-link capacitance.

In order to obtain a simple and efficient control strategy for three-phase two-level grid-connected power converters with improved system performance including reduced computation and communication burdens, an event-triggered continuous control set-model predictive control (CCS-MPC) is proposed in this paper. In the DC-link voltage regulation loop, a simple but efficient controller is designed to track the DC-link voltage to its reference value. In the power tracking loop, a simple event-triggered CCS-MPC is utilized to ensure that the active power and reactive power also track their reference values. The control signals to the converter are updated and transmitted only when the triggering condition is satisfied. Compared with periodic sampling control strategies, which require the calculation and transmission of control signals in each sampling period, the proposed controller reduces the utilization of limited computation and communication resources while maintaining good tracking performance. By comparing with the periodic sampling proportional-integral strategy, the effectiveness and superiority of the proposed strategy are shown through simulation results.

This article investigates the problem of optimal tracking control for cyber-physical systems (CPSs) when the cyber realm is attacked by Denial-of-Service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and analyze the impact of DoS attacks on tracking performance. First, a Riccati equation for the augmented system, including the system model and the reference model is derived under the framework of dynamic programming. The existence and uniqueness of its solution are proved. Second, the impact of the successful DoS attack probability on tracking performance is analyzed. A critical value of the probability is given, beyond which the solution to the Riccati equation cannot converge. The tracking controller cannot be designed. Third, reinforcement learning is introduced to design the optimal tracking control schemes, in which the system dynamics are not necessary to be known. Finally, both a dc motor and an F16 aircraft are used to evaluate the proposed control schemes in this article.

With the participation of a significant number of plug-in electric vehicles (PEVs), it is really challenging to achieve economic-effective in load frequency control (LFC) while sustaining satisfiable system performance. To tackle this challenge, a new distributed economic model predictive control (DEMPC) strategy is proposed for the LFC with the large-scale PEV participation. In the light of the vulnerability of LFC to false data injection (FDI) attacks, a model-based χ2intrusion detection unit is integrated with the proposed DEMPC. This model-based intrusion detection unit can not only monitor the FDI attacks, but also generate a model-based state prediction for the DEMPC once the data is identified as compromised. Then, an event-triggering mechanism is presented to reduce the computation and communication burdens of each area controller. Simulation studies of a four-area power system are conducted and the results validate the effectiveness of the proposed intrusion detection unit and event-triggering conditions for the DEMPC.

This paper studies the event-triggered robust model predictive control (MPC) problem for constrained linear systems subject to bounded disturbances. For control computations, explicit MPC reduces online optimizations to simple function evaluations, thus is suitable for resource constrained systems. We propose an event-triggering mechanism (ETM) for an explicit robust model predictive controller such that function evaluations are not needed at every time instant and the ETM itself requires comparably less online computations. In the proposed ETM, we design a contracting polytopic trigger set with respect to system constraints and the explicit robust control law. The resulting event-triggered control scheme ensures robust constraint satisfaction and robust stability for the closed-loop system while avoiding the Zeno-like behavior. Simulation results illustrate the validity of the event-triggered robust control scheme.

This paper deals with the problem of dissipativity-based filtering for switched genetic regulatory networks (GRNs) with stochastic perturbation and time-varying delays. By choosing an appropriate piecewise Lyapunov function and using the average dwell time method, we propose a new set of sufficient conditions in terms of Linear matrix inequalities (LMIs) for the existence of dissipative filter, which ensures that the resulting filtering error system is mean-square exponentially stable with dissipativity performance. The filter gains are provided by solving feasible solutions to a certain set of LMIs. A simulation example is given to demonstrate the effectiveness of the desired dissipativity-based filter design approach.

In this article, we investigate the problem of the dissipativity-based resilient sliding-mode control design of cyber-physical systems with the occurrence of denial-of-service (DoS) attacks. First, we analyze the physical layer operating without DoS attacks to ensure the input-to-state practical stability (ISpS). The upper bound of the sample-data rate in this situation can be identified synchronously. Next, for systems under DoS attacks, we present the following results: 1) combined with reasonable hypotheses of DoS attacks, the ISpS as well as dissipativity of the underlying system can be guaranteed; 2) the upper bound of the sample-data rate in the presence of DoS attacks can be derived; and 3) the sliding-mode controller is synthesized to achieve the desired goals in a finite time. Finally, two examples are given to illustrate the applicability of our theoretical derivation.

This article is concerned with the problem of the Hankel-norm model reduction for stochastic discrete-time nonlinear systems in interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy framework. The IT2 T-S fuzzy model is an efficient model for describing uncertain nonlinear systems, and the model reduction is to simplify the high-order complex systems by reducing the order of the original system. The aim of this article is to reduce the order of the original stochastic discrete-time IT2 fuzzy system into lower order system without ignoring the influence of IT2 membership functions. First, the Hankel-norm performance of the stochastic discrete-time IT2 fuzzy model is analyzed. Then, based on the projection theorem and cone complementary linearization approach, a convex Hankel-norm-based model reduction approach subject to conditions in the form of linear matrix inequalities (LMIs) is obtained. A membership-functions-dependent (MFD) technique is applied to capture the information of IT2 membership functions and further reduce the conservativeness. A numerical example is presented to illustrate the effectiveness of the proposed results.

This paper presents a tracking control scheme with quantization mechanism for hypersonic flight vehicles (HFVs) with prescribed performance using an interval type-2 fuzzy neural network (IT2FNN). A parameterized tracking error model of the HFV is derived with some considered uncertainties, which are approximated by an IT2FNN. The tracking control of the velocity and altitude of the HFV is designed by using a prescribed performance control technique. It allows that transient characteristics of the tracking errors can be improved and adjusted by some prescribed performance functions. According to an adaptive backstepping control design procedure, novel continuous control laws of the fuel equivalency ratio, canard deflection, and elevator deflection are designed with logarithmic quantization mechanism, for the sake of avoiding inadvertently increasing the effective gains of continuous controllers as well as reducing loads of the communication from controller unit to actuator unit. Besides, the limited tracking errors of the flight path angle and angle-of-attack can be achieved by applying the designed controllers. Finally, the presented tracking controllers with quantization mechanism are validated by comparative simulations.

This paper investigates the problem of output feedback sliding mode control (SMC) for a class of uncertain nonlinear systems through Takagi-Sugeno fuzzy affine models. By adopting a state-input augmentation method, a descriptor system is first constructed to characterize the dynamical properties of the sliding motion. Based on a common quadratic Lyapunov function and piecewise quadratic Lyapunov functions, sufficient conditions for asymptotic stability analysis of the sliding motion are obtained with some convexification techniques. An output feedback dynamic SMC design scheme is proposed to force the states of the resulting closed-loop system onto the sliding surface locally in finite time. Two simulation examples are finally shown to illustrate the effectiveness of the proposed approaches.

This paper proposes a dynamic sliding mode control (SMC) approach to the robust voltage regulation of dc-dc boost converters by using interval type-2 fuzzy neural networks (IT2FNNs). First, uncertainties caused by the perturbation of the input inductor and the output capacitor are represented with some bounded approximation errors, by the utilization of a Takagi-Sugeno (T-S) fuzzy modeling approach. Based on the represented model of the boost converter, a new type of sliding surface is designed depending on the duty cycle and reference inputs of the converter. Then, a dynamic SMC law is designed, by considering that the perturbation of the uncertain parameters, including input inductor, output capacitor, load resistor, and input voltage, is bounded. Meanwhile, we adopt an exponential plus power approaching law in the sliding mode controller for fast reachability of the sliding surface and a small chattering in the duty cycle input. Moreover, in terms of the considered uncertainties, a novel IT2FNN-based dynamic SMC law is derived, by applying simplified ellipsoidal-type membership functions in the type-2 fuzzy neural network. To improve the capacity to manage the uncertainties, some online learning algorithms for the updating of the IT2FNN are designed by a gradient descent method (GDM), without the requirement of the boundedness of the uncertainties. The resulting tracking error system is synthesized to be bounded stable based on the designed IT2FNN-based dynamic SMC. Finally, the effectiveness of the proposed adaptive IT2FNN-based dynamic SMC method is verified by some comparative simulation results.

This paper presents voltage regulation and current tracking control strategies for three phase two-level grid-connected power converters. By using power-invariant Park's transformation, an averaged mathematical model of power converters is obtained in dq synchronous reference frame. Then a novel control strategy using adaptive control and H∞technique is proposed to regulate the dc-link output voltage as well as track a desired current reference for three-phase power rectifiers. More specifically, an efficient adaptive controller is established in the external loop for regulating dc-link output voltage in the presence of external disturbances. A set of H∞controllers are designed in the internal loop to force the input currents track their desired values. Finally, simulation results obtained from the proposed control method are presented, analyzed, and compared with that of sliding mode control, and the superiority of the proposed control laws is verified.

This article focuses on the model reduction problem of discrete-time time-delay interval type-2 Takagi-Sugeno (T-S) fuzzy systems. Compared with the type-1 T-S fuzzy system, the interval type-2 T-S fuzzy system has more advantages in expressing nonlinearity and capturing uncertainties. In addition, in order to simplify the analysis process, complex high-order systems can be approximated as low-order systems, which is called model reduction. In previous studies, there are few researches on model reduction of the interval type-2 T-S fuzzy system with time delay. Hankel norm is adopted to limit the error after model reduction. Based on Jensen's inequality, a linear matrix inequality (LMI) condition for the Hankel norm performance of the error system is obtained. A membership-function-dependent method based on piecewise linear membership functions is utilized to deal with mismatched membership functions where information of membership functions will be used for relaxing analysis results. Next, by a convex linearization design, the model reduction problem is formulated as a convex LMI feasibility/optimization condition. Numerical examples are given to verify the validity of the analysis.

This paper investigates the problem of the resilient control for cyber-physical systems (CPSs) in the presence of malicious sensor denial-of-service (DoS) attacks, which result in the loss of state information. The concepts of DoS frequency and DoS duration are introduced to describe the DoS attacks. According to the attack situation, that is, whether the attack is successfully implemented or not, the original physical system is rewritten as a switched version. A resilient sliding mode control scheme is designed to guarantee that the physical process is exponentially stable, which is a foundation of the main results. Then, a zero-sum game is employed to establish an effective mixed defense mechanism. Furthermore, a defense-based resilient sliding mode control scheme is proposed and the desired control performance is achieved. Compared with the existing results, the differences mainly lie in two aspects, that is, one where a switched model is obtained, based on which the average dwell-time like approach is utilized to derive the resilient control scheme, and the other where the zero-sum game in employed to make the attacks satisfy the concepts of DoS frequency and DoS duration. Finally, simulation results are given to demonstrate the effectiveness of the proposed resilient control approach.

In this article, a linear extended state observer (LESO) based second-order sliding mode (SOSM) control strategy with the direct power control is proposed for a three-phase neutral-point-clamped (NPC) power converter connected to a dc microgrid. Comparing with the PI control method, the proposed approach implements the advanced SOSM controller into the voltage regulation loop and instantaneous power tracking loop to enhance the dynamic and steady state performance. Furthermore, saturation function is applied in the SOSM method to weaken the chattering phenomenon. On the other hand, since the dc load is regarded as an external disturbance, an efficient LESO is designed in the voltage regulation loop to reject this disturbance. The design process of the proposed control strategy is shown based on the continuous averaged model of the NPC converter. Finally, comparison experiments among PI, LESO-based PI, and proposed LESO-based SOSM control strategies are implemented, which validate the superiority of the proposed approach.

In this paper, one new distributed load frequency regulation approach is proposed for smart power system operation under two specific practical constraints, including the limited communication resource and speed droop parametric uncertainty. To address these two constraints, the co-design of event-triggering communication scheme and distributed model-based controller is studied. Instead of using zero-order holders, the proposed model-based scheme is able to extend the maximum allowable time interval and thus reduce communication bandwidth usage. In the meantime, the proposed co-design scheme is able to get the model-based control parameters and event-triggering condition metrics simultaneously. This can loosen the conservation in the choice of control gains and event-triggering parameters faced by existing approaches where the control gains are fixed in prior. Comparisons on the multiple-area system confirm that this designed load frequency regulation method significantly reduces the number of required data transmissions without sacrificing the dynamic performance of the frequency and tie-line power. It is also shown that the proposed approach has great robustness to speed droop coefficient uncertainty.

This paper addresses the fault tolerant control problem for high-speed trains in case of multiple possible failures. A new multiple point-mass model with system faults is built based on a stochastic jump system model approach. A novel active fault tolerant composite hierarchical anti-disturbance control strategy based on the disturbance observer is proposed such that the resulting composite system is stochastically stable with position and velocity tracking performance. According to whether the transition probabilities (TPs) of the failure and fault detection and isolation process can be accessed completely, three different cases (TPs are completely known, partially known, and completely unknown) are analyzed. For each case, based on the Lyapunov functional approach, a composite hierarchical controller is synthesized via a convex optimization problem. Finally, the simulations are given to illustrate the performance of the proposed methodologies.

With rapid development of deep integration of computation, control, and communication, Cyber-Physical Systems (CPSs) play an important role in industrial processes. Combined with the technology of fog computing, CPSs can outsource their complicated computation to the fog layer, which in turn, may bring security threats with regard to data privacy. To protect data privacy in a control framework, this paper investigate observer-based secure control problem towards fog computing aided CPSs (FCA-CPSs) by utilizing data perturbation method. Firstly, security inputs are designed to encrypt the transmitted states to realize specific confidentiality level. Then, sufficient conditions are established to ensure the stability of considered FCA-CPSs. Finally, a numerical example is provided to illustrate the effectiveness of the secure estimation scheme.

This paper presents an adaptive sliding mode observer (ASMO) to estimate three-phase grid voltage parameters, including both positive and negative sequences of voltage and grid frequency under unbalanced grid faults. First, the dynamic of three-phase voltage is reformulated as the second-order uncertain system, which can transform the traditional phase locked loop problem to the observer design problem. Based on the obtained dynamic system, an ASMO is constructed to estimate three-phase grid voltage parameters, using the adaptive and sliding mode techniques. The stability of the overall system including the observer estimation errors, sliding variable and adaptive estimation errors is rigorously proved by Lyapunov stability theory. The performance of proposed observer is assessed for estimation of three-phase grid voltage parameters by simulation in which two types of faults, i.e., the amplitude of voltage and grid frequency variations. are considered.

This paper is concerned with the stabilization problem of type-2 fuzzy systems with network-induced packet losses. By regarding the packet lost process as an unstable mode of a switched system, the stability of the system is then guaranteed with the aid of the mode-dependent average dwell time approach in the sense of the slow and fast switching. The discrete-time multiple discontinuous Lyapunov function is also utilized for the analysis. Two sufficient conditions regarding the stability and the stabilization of the system are proposed. The state-feedback matrices can be then calculated from the conditions to ensure the criterion that the packet-loss rate is no larger than a specific constant. Two practical examples are given to illustrate the feasibility and effectiveness of the proposed method.

As communication networks are increasingly implemented to support the information exchange between microgrid control centers and/or local controllers, they expose microgrids to cyber-attack threats. This paper aims to analyze the stochastic stability of islanded microgrids in the presence of random denial of service (DoS) attack and propose a mode-dependent resilient controller to mitigate the influence of DoS attacks. Specifically, the small-signal model of the microgrid under the DoS attack is integrated as a stochastic jump system with state continuity disruptions. A new vulnerability metric is defined by using observability Gramians of the stochastic jump system, to measure the vulnerability of the system regarding DoS attack choices. The Lyapunov function analysis is conducted to find conditions sustaining the stochastic stability of the islanded microgrid in the form of linear matrix inequalities. A mode-dependent control approach is proposed for microgrids to mitigate the influence of random DoS attacks. In case studies, the vulnerability analysis and time-domain simulation results show the performance of the investigated microgrid can be degraded when the random DoS attacks exist. When the proposed mode-based secondary frequency controllers are installed, the islanded microgrid can sustain its stability during the attacking period and system dynamics rapidly converge when the DoS attack is over.

Cascaded H-bridge (CHB) and modular multilevel converter (MMC) are effective solutions for medium and high voltage motor drive applications. However, the choice between the two converters is not particularly clear. This paper presents a loss evaluation of cascaded H-bridge and modular multilevel converter as a reference for loss calculation. A brief description of CHB and MMC are provided and the explicit expression of conduction loss and switching loss for MMC and CHB is derived respectively to show the relationship between the loss and the main variables of MMC and CHB directly based on carrier phase-shifting PWM (CPS-PWM). In the derivation of switching loss, a novel switching loss calculation method based on PWM is proposed to simplify the theoretical calculation process. Finally, the total loss comparison of the two converters based on different conditions are shown for a better selection in motor drive application.

This paper presents a novel control scheme for DC-DC boost converter, maintaining the desirable voltage regulation performance under high load variation and large change of voltage reference. The model of converter is reformulated, in which the unknown equivalent load, input voltage, model uncertainties and unmodeled dynamics are lumped as external disturbance. The control strategy is designed with backstepping control technology, similar to the cascade control method in which the intermediate variable is introduced to fast respond the control demand, effectively dealing with the nonlinearity of the boost converter dynamics. The disturbance observers are established to estimate the lumped disturbances, rejecting disturbances and removing steady-state errors to improve the closed-loop performance. The simulation results demonstrate that the proposed control strategy, backstepping control combined with disturbance observer, provides lots of advantages superior to the conventional PI control such as faster dynamic response and less output voltage drop.

This paper addresses the problem of delay-dependent robust and reliable $\mathscr {H}_{\infty }$ static output feedback (SOF) control for a class of uncertain discrete-time Takagi-Sugeno fuzzy-affine (FA) systems with time-varying delay and actuator faults in a singular system framework. The Markov chain is employed to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular FA system. By constructing a piecewise-Markovian Lyapunov-Krasovskii functional, a new $\mathscr {H}_{\infty }$ performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the piecewise-affine SOF controller design is proposed via a convex program. Lastly, illustrative examples are given to show the efficacy and less conservatism of the presented approach.

This paper is concerned with the problem of secure state reconstruction for cyber-physical systems (CPSs). CPSs are more vulnerable to the cyber world yet to attackers, who can attack any sensor of the considered systems and modify values of attacked sensors to be arbitrary ones. In the design process, both malicious attacks on sensors and unknown input are taken into consideration. First, a linear discrete-time state-space model is utilized to describe such systems, and then a sparse vector is adopted to model attacks. By collecting sensor measurements and using an iterative approach, a new model in descriptor form is obtained, which paves the way for estimating system states under an unknown input situation. Second, the problem of secure state estimation is transformed into an optimal version. A novel sliding-mode observer is proposed to estimate system states from collected sensor measurements corrupted by malicious attacks. In order to guarantee the estimations to be sparse, a projection operator is designed. Third, a projected sliding-mode observer-based estimation algorithm is developed to reconstruct system states, where an event-triggered scheme is integrated to save limited computational resource. In addition to propose such an algorithm, the effectiveness of both projection operator and sliding-mode observer is analyzed. Furthermore, the convergence of the proposed secure estimation algorithm is proved. Finally, some simulation results are given to demonstrate the effectiveness of the proposed algorithm.

This paper is centered upon the state estimation for delayed genetic regulatory networks. Our aim is at estimating the concentrations of mRNAs and proteins by designing reduced-order and full-order state observers based on available network outputs. We introduce a Lyapunov-Krasovskii functional including quadruplicate integrals, and estimate its derivative by employing the Wirtinger-type integral inequalities, reciprocal convex technique, and convex technique. From which, delay-dependent sufficient conditions, in the form of linear matrix inequalities (LMIs), are investigated to ensure that the resultant error system is asymptotically stable. One can verify these conditions by utilizing the MATLAB Toolboxes LMI or YALMIP. In addition, the gains of reduced-order and full-order observers are represented by the feasible solutions of the LMIs, and thereby, the concrete expressions of the desired reduced-order and full-order state observers are presented. Finally, the simulation results of a numerical example are demonstrated, which explains the validity of the proposed method.

This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi-Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation error dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.

This paper is concerned with the optimal guaranteed cost sliding-mode control problem for interval type-2 (IT2) Takagi-Sugeno fuzzy systems with time-varying delays and exogenous disturbances. In the presence of the uncertain parameters hidden in membership functions, an adaptive method is presented to handle the time-varying weight coefficients reflecting the change of the uncertain parameters. A new integral sliding surface is presented based on the system output. By designing a novel adaptive sliding-mode controller, system perturbation or modeling error can be compensated, and the reachability of the sliding surface can be guaranteed with the ultimate uniform boundedness of the closed-loop system. Optimal conditions of an H2guaranteed cost function and an H∞performance index are established for the resulting time-delay control system. Finally, an inverted pendulum system represented by the IT2 fuzzy model is applied to illustrate the advantages and effectiveness of the proposed control scheme.

This paper attempts to propose a new solution to the event-triggered fault detection problem for discrete-time Takagi-Sugeno fuzzy systems in a network environment. First, for the original Takagi-Sugeno fuzzy system, our focus is on constructing a new based-network residual system, considering the event-triggering mechanism, interval time-varying delays, and packet dropouts. Under the established system, a less conservative basis-dependent stability condition is obtained by using the reciprocally convex technique, which ensures that the corresponding residual system is mean-square asymptotically stable with a given $\mathcal{H}_{\infty }$ performance. Second, the desired fuzzy-rule-dependent fault detector and the controller scheme are established using a variable substitution approach. Furthermore, these criteria can be transformed into convex optimization problems and then calculated by the standard optimization toolbox. Finally, the advantages of the proposed fault detector and controller coordinated design technique are illustrated by the simulation results.

This paper addresses the model reduction problem of discrete-time interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy systems, which represent the discrete-time nonlinear systems subject to uncertainty. With the use of IT2 fuzzy sets, the uncertainty of the discrete-time nonlinear system can be captured by the lower and upper membership functions. For a given high-order discrete-time IT2 T–S fuzzy system, the purpose is to find a lower dimensional system to approximate the original system. To achieve the approximation performance, an$\mathcal {H}_\infty$norm is used to suppress the error between the original system and its simplified system. By introducing a membership-functions-dependent technique and applying a convex linearization method, a membership-functions-dependent condition, which takes the information of membership functions into account, is obtained to reduce the dimensions of system matrices and the number of fuzzy rules of the system. All the obtained theorems are represented as in the form of linear matrix inequalities. Finally, simulation results are demonstrated to show the effectiveness of the derived results.

This paper studies an output-based adaptive fault-tolerant control problem for nonlinear systems with nonstrict-feedback form. Neural networks are utilized to identify the unknown nonlinear characteristics in the system. An observer and a general fault model are constructed to estimate the unavailable states and describe the fault, respectively. Adaptive parameters are constructed to overcome the difficulties in the design process for nonstrict-feedback systems. Meanwhile, dynamic surface control technique is introduced to avoid the problem of "explosion of complexity". Furthermore, based on adaptive backstepping control method, an output-based adaptive neural tracking control strategy is developed for the considered system against actuator fault, which can ensure that all the signals in the resulting closed-loop system are bounded, and the system output signal can be regulated to follow the response of the given reference signal with a small error. Finally, the simulation results are provided to validate the effectiveness of the control strategy proposed in this paper.

The model approximation problem is studied in this paper for switched genetic regulatory networks (GRNs) with time-varying delays. We focus on constructing a reduced-order model to approximate the high-order GRNs considered under the switching signal subject to certain constraints, such that the approximation error system between the original and reduced-order systems is exponentially stable with a disturbance attenuation performance. The stability conditions and the disturbance attenuation performance are established by utilizing two integral inequality bounding techniques and the average dwell-time method for the approximation error system. Then, the solvability conditions for the reduced-order models for the GRNs are also established using the projection method. Furthermore, the model approximation problem can be transferred into a sequential minimization problem that is subject to linear matrix inequality constraints by using the cone complementarity algorithm. Finally, several examples are provided to illustrate the effectiveness and the advantages of the proposed methods.

This paper is concerned with the exponential stability analysis of genetic regulatory networks (GRNs) with switching parameters and time delays. In this paper, a new integral inequality and an improved reciprocally convex combination inequality are considered. By using the average dwell time approach together with a novel Lyapunov-Krasovskii functional, we derived some conditions to ensure the switched GRNs with switching parameters and time delays are exponentially stable. Finally, we give two numerical examples to clarify that our derived results are effective.

This paper addresses the problem of state estimation for delayed genetic regulatory networks (DGRNs) with reaction-diffusion terms using Dirichlet boundary conditions. The nonlinear regulation function of DGRNs is assumed to exhibit the Hill form. The aim of this paper is to design a state observer to estimate the concentrations of mRNAs and proteins via available measurement techniques. By introducing novel integral terms into the Lyapunov-Krasovskii functional and by employing the Wirtinger-type integral inequality, the convex approach, Green's identity, the reciprocally convex approach, and Wirtinger's inequality, an asymptotic stability criterion of the error system was established in terms of linear matrix inequalities (LMIs). The stability criterion depends upon the bounds of delays and their derivatives. It should be noted that if the set of LMIs is feasible, then the desired observation of DGRNs is possible, and the state estimation can be determined. Finally, two numerical examples are presented to illustrate the availability and applicability of the proposed scheme design.

A new approach to the control of three-phase two-level grid-connected power converters is proposed in this paper. The proposed control is an extended state observer (ESO)based second order sliding mode (SOSM), which comprises two control loops: the outer loop is a voltage regulation loop, as well as inner loop is an instantaneous power tracking loop. The outer loop is accomplished by an H∞controller plus an ESO, which is designed to regulate dc-link capacitor voltage of the converter and asymptotically reject external disturbances and parameter perturbations. The SOSM strategy is employed in the inner loop to drive the active and reactive power convergence to their desired values. Control objectives of nearly unity power factor and dc-link capacitor voltage regulation are simultaneously satisfied. Availability of the ESO-based SOSM is compared with the classic proportional-integral control in simulations, and the comparison implies that the proposed strategy not merely achieves an almost perfect tracking performance, but also provides a complete robustness against resistance load variation.

This paper employs second-order sliding mode control (SOSMC) to carry out the current tracking and voltage regulation tasks of a grid-connected three-phase two-level power converter. For dc-link voltage regulation, a disturbance observer is adopted to improve the whole control performance. In this paper, four different types of disturbance observers are proposed for comparison, which are conventional linear observer (LINO), second-order sliding mode observer (SOSMO), linear extended state observer (LESO) and nonlinear extended state observer (NESO). To ensure fair comparison, the parameters of the observers are tuned to make the power converter achieve almost same current total harmonic distortion (THD) and steady error of dc-link voltage. The performances are compared in the term of transient response of the dc-link voltage. The simulation results show: first, the disturbance observers improve the control performance of SOSMC; second, SOSMO outperforms the other three observers.

In this work, the control problem for type-2 T-S fuzzy system with packet dropouts is investigated by modeling the system as a switched system with an unstable subsystem. The mode-dependent average dwell time approach in both slow and fast switching sense is utilized for the analysis and synthesis. A sufficient condition is given by ensuring the packet loss rate no bigger than the specific fast switching mode-dependent average dwell time (MDADT) and the corresponding feedback matrices are obtained. Several simulation results illustrate the feasibility and effectiveness of the proposed method and the priority of the type-2 fuzzy system on describing some nonlinear systems.

In this paper, a novel scheme of disturbance observer based second order sliding mode (SOSM) control for DC-DC buck converters is proposed. A cascade-control structure is established to regulate the output voltage and force the inductor current to track its reference, which comprises two control loops. The voltage regulation loop which is based on an SOSM controller combined with extended state observer (ESO) is the external loop. The current tracking loop also accomplished by SOSM controller is the internal loop. The fast motion is dominated by the dynamics of the current tracking loop whereas the slow motion stems from the dynamics of the output voltage. In addition, the load resistance that influences significantly the dynamics of whole system is regarded as the external disturbance in this papper. A disturbance observer, ESO, aims at asymptotically rejecting disturbances to converter. The proposed control strategy is verified using simulation test.

This paper is concerned with the finite-time stability problem of the delayed genetic regulatory networks (GRNs) with reaction-diffusion terms under Dirichlet boundary conditions. By constructing a Lyapunov-Krasovskii functional including quad-slope integrations, we establish delay-dependent finite-time stability criteria by employing the Wirtinger-type integral inequality, Gronwall inequality, convex technique, and reciprocally convex technique. In addition, the obtained criteria are also reaction-diffusion-dependent. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

This paper studies the observer-based tracking control problem for discrete-time nonlinear networked control systems with parameter uncertainties and unmeasurable state variables. A network-induced constraint, i.e., the intermittent measurement loss, is considered in the controller design. The uncertain nonlinear system is described by an interval type-2 (IT2) fuzzy Takagi-Sugeno model, in which the lower and the upper membership functions with corresponding coefficients are used to capture and express uncertainties existing in the system. A premise-variables-independent IT2 fuzzy observer is constructed to estimate the unmeasurable state variables, and then a novel IT2 fuzzy tracking controller is designed. Furthermore, sufficient criteria are established to guarantee the resulting closed-loop system to be stochastically stable. Finally, two examples are provided to show the effectiveness of the proposed approach.

This paper develops a new fuzzy dynamic outputfeedback control scheme for Takagi-Sugeno (T-S) fuzzy systems with time-varying input delay and output constraints based on (Q, S, R)-α-dissipativity. The proposed controller, called a (Q, S, R)-α-dissipative output-feedback fuzzy controller, takes into consideration the abstract energy, storage function, and supply rate for the disturbance attenuation and provides a unified framework that can incorporate existing results for H∞ and passivity controllers as special cases for T-S fuzzy systems with time-varying input delay and output constraints. A dynamic parallel distributed compensator is used to design the (Q, S, R)-α-dissipative output-feedback fuzzy controller to ensure the asymptotic stability and strict (Q, S, R)-α-dissipativity of closed-loop systems described by a T-S fuzzy model that satisfies some output constraints. By employing the reciprocally convex approach, a new set of delay-dependent conditions for the desired controller is formulated in terms of the linear matrix inequality. The effectiveness and the applicability of the proposed design techniques are validated by an example of control for active suspension systems for different road conditions.

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.

This paper is concerned with the event-triggered H∞control problem for discrete-time nonlinear networked control systems with unreliable communication links. First, an event-triggered scheme is proposed to determine whether the sampled data should be released into the network or not. Second, when the released data is transmitted in the network, a Bernoulli process is employed to model the phenomenon of data losses. Third, considering the instants at which the sampled data is not released or data losses occur, a new random process is first developed to model the input data sequence of the controller under the effect of the buffer. Consequently, a novel method is presented to address the stability analysis and control synthesis problems based on the polynomial fuzzy model approach. Finally, some simulation results are given to illustrate the effectiveness of the proposed method.

This paper deals with the problem of reliable and robust H∞static output feedback (SOF) controller synthesis for continuous-time nonlinear stochastic systems with actuator faults. The nonlinear stochastic plant is expressed by an Itô-type Takagi-Sugeno fuzzy-affine model with parametric uncertainties, and a Markov process is employed to model the occurrence of actuator fault. The purpose is to design an admissible piecewise SOF controller, such that the resulting closed-loop system is stochastically stable with a prescribed disturbance attenuation level in an sense. Specifically, based on a Markovian Lyapunov function combined with Itô differential formula, S-procedure, and some matrix inequality convexification procedures, two new approaches to the reliable SOF controller analysis and synthesis are proposed for the underlying stochastic fuzzy-affine systems. It is shown that the existence of desired reliable controllers is fully characterized in terms of strict linear matrix inequalities. Finally, simulation examples are presented to illustrate the effectiveness and advantages of the developed methods.

This paper studies the problem of reliable filter problem for a category of sensor networks in the framework of interval type-2 fuzzy model. In the filter design, the random link failures, which are caused possibly by missing measurements as well as by probabilistic communication failures, are considered to illustrate more realistic dynamical behaviors of sensor networks. In order to tackle the uncertainties existing in systems, interval type-2 (IT2) fuzzy approach is utilized to establish the model, wherein upper and lower membership functions together with weighting coefficients are employed to express the uncertainties. An distributed IT2 fuzzy filter model is constructed to estimate system states. Using the Lyapunov theory, sufficient conditions have been given to ensure that the filtering error system is mean-square asymptotically stable and satisfies the predefined average $ \mathcal {H}_{\infty }$ performance level. Moreover, the criteria to design the filter parameters are developed through using cone complementary linearization approach. Finally, a practical example is given to validate the proposed method.

This paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.

This paper studies the problem of adaptive fuzzy control for a category of single-input single-output nonlinear networked control systems with network-induced delay and data loss based on adaptive backstepping control approach. Fuzzy logic systems are used to approximate the unknown nonlinear characteristics existing in the system, while Pade approximation is introduced to handle network-induced delay. Data loss occurs intermittently and stochastically in the data transmitting process, which is regarded as the delay in the controller design. In the framework of adaptive fuzzy backstepping technique, a novel state-feedback adaptive controller is constructed to ensure all signals in the resulting closed-loop system to be bounded and the state variables can be regulated to the origin. Finally, two examples are given to show the validity of the proposed results.

This paper presents an idea to further simplify and relax the linear matrix inequality (LMI) stability conditions of Takagi-Sugeno (T-S) fuzzy systems. By considering the distribution of membership functions in a unified space, we can easily find the overall relation of the original nonlinear system and its approximated local subsystems. Based on the theory of convex combination, the upper-bounds and lower-bounds of each membership functions can be directly used to construct less conservative LMI conditions. The cases of both Type-1 and Type-2 systems are considered, and examples are provided to illustrate the achieved improvement.

In this paper, a novel adaptive sliding mode controller is designed against the actuator saturation for a PEM fuel cell air-feed system. To deal with the saturation nonlinearity, a diagonal matrix with unknown elements is introduced for adaptive design in terms of sliding mode control strategy. An integral-type sliding mode surface is designed with constrained conditions. The designed SMC law can guarantee the finite-time convergence against the actuator saturation of the plant. The effectiveness of the proposed saturated SMC scheme is validated in the application to air-feed PEM fuel cell systems.

This paper deals with the problem of globally asymptotic stability for nonnegative equilibrium points of genetic regulatory networks (GRNs) with mixed delays (i.e., time-varying discrete delays and constant distributed delays). Up to now, all existing stability criteria for equilibrium points of the kind of considered GRNs are in the form of the linear matrix inequalities (LMIs). In this paper, the Brouwer's fixed point theorem is employed to obtain sufficient conditions such that the kind of GRNs under consideration here has at least one nonnegative equilibrium point. Then, by using the nonsingular M-matrix theory and the functional differential equation theory, M-matrix-based sufficient conditions are proposed to guarantee that the kind of GRNs under consideration here has a unique nonnegative equilibrium point which is globally asymptotically stable. The M-matrix-based sufficient conditions derived here are to check whether a constant matrix is a nonsingular M-matrix, which can be easily verified, as there are many equivalent statements on the nonsingular M-matrices. So, in terms of computational complexity, the M-matrix-based stability criteria established in this paper are superior to the LMI-based ones in literature. To illustrate the effectiveness of the approach proposed in this paper, several numerical examples and their simulations are given.

In this paper, the problem of fuzzy filter design is investigated for a class of nonlinear networked systems on the basis of the interval type-2 (IT2) fuzzy set theory. In the design process, two vital factors, intermittent data packet dropouts and quantization, are taken into consideration. The parameter uncertainties are handled effectively by the IT2 membership functions determined by lower and upper membership functions and relative weighting functions. A novel fuzzy filter is designed to guarantee the error system to be stochastically stable with H∞ performance. Moreover, the filter does not need to share the same membership functions and number of fuzzy rules as those of the plant. Finally, illustrative examples are provided to illustrate the effectiveness of the method proposed in this paper.

This paper focuses on stability analysis for a class of genetic regulatory networks with interval time-varying delays. An improved integral inequality concerning on double-integral items is first established. Then, we use the improved integral inequality to deal with the resultant double-integral items in the derivative of the involved Lyapunov-Krasovskii functional. As a result, a delay-range-dependent and delay-rate-dependent asymptotical stability criterion is established for genetic regulatory networks with differential time-varying delays. Furthermore, it is theoretically proven that the stability criterion proposed here is less conservative than the corresponding one in [Neurocomputing, 2012, 93: 19-26]. Based on the obtained result, another stability criterion is given under the case that the information of the derivatives of delays is unknown. Finally, the effectiveness of the approach proposed in this paper is illustrated by a pair of numerical examples which give the comparisons of stability criteria proposed in this paper and some literature.

This paper is concerned with the problems of receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay. New delay-dependent conditions on the terminal weighting matrices of a new finite horizon cost functional for receding horizon stabilization are established for neural networks with time-varying or time-invariant delays using single- and double-integral Wirtinger-type inequalities. Based on the results, delay-dependent sufficient conditions for the receding horizon disturbance attenuation are given to guarantee the infinite horizon H∞ performance of neural networks with time-varying or time-invariant delays. Three numerical examples are provided to illustrate the effectiveness of the proposed approach.

This paper focuses on the problem of fault detection for Takagi-Sugeno fuzzy systems with time-varying delays via delta operator approach. By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem. The time-varying delay is approximated by the two-term approximation method. Fuzzy augmented fault detection system is constructed in δ -domain, and a threshold function is given. By applying the scaled small gain theorem and choosing a Lyapunov-Krasovskii functional in δ -domain, a sufficient condition of asymptotic stability with a prescribed H∞ disturbance attenuation level is derived for the proposed fault detection system. Then, a solvability condition for the designed fault detection filter is established, with which the desired filter can be obtained by solving a convex optimization problem. Finally, an example is given to demonstrate the feasibility and effectiveness of the proposed method.

This paper presents the stability analysis of polynomial fuzzy-model-based (PFMB) control systems, in which both the polynomial fuzzy model and the polynomial fuzzy controller are allowed to have their own set of premise membership functions. In order to address the input nonlinearity, the control signal is considered to be bounded by a sector with nonlinear bounds. These nonlinear lower and upper bounds of the sector are constructed by combining local bounds using fuzzy blending such that local information of input nonlinearity can be taken into account. With the consideration of imperfectly matched membership functions and input nonlinearity, the applicability of the PFMB control scheme can be further enhanced. To facilitate the stability analysis, a general form of approximated membership functions representing the original ones is introduced. As a result, approximated membership functions can be brought into the stability analysis leading to relaxed stability conditions. The sum-of-squares approach is employed to obtain the stability conditions based on Lyapunov stability theory. Simulation examples are presented to demonstrate the feasibility of the proposed method.

This paper investigates the stability of a polynomial-fuzzy-model-based (PFMB) control system formed by a nonlinear plant represented by a polynomial fuzzy model and a polynomial fuzzy controller connected in a closed loop. Three cases of polynomial fuzzy controllers are proposed for the control process with the consideration of a matched/mismatched number of rules and/or premise membership functions, which demonstrate different levels of controller complexity, design flexibility, and stability analysis results. A general polynomial Lyapunov function candidate is proposed to investigate the system stability. Unlike the published work, there is no constraint on the polynomial Lyapunov function candidate, which is independent of the form of the polynomial fuzzy model. Thus, it can be applied to a wider class of PFMB control systems and potentially produces more relaxed stability analysis results. Two-step stability conditions in terms of sum-of-squares (SOS) are obtained to numerically find a feasible solution. To facilitate the stability analysis and relax the stability analysis result, the boundary information of membership functions is taken into account in the stability analysis and incorporated into the SOS-based stability conditions. Simulation examples are given to illustrate the effectiveness of the proposed approach.

This paper is concerned with the problems of state and output feedback control for interval type-2 (IT2) fuzzy systems with mismatched membership functions. The IT2 fuzzy model and the IT2 state and output feedback controllers do not share the same membership functions. A novel performance index, which is expressed as an extended dissipativity performance, is introduced to be a generalization of H∞, L2-L∞, passive, and dissipativity performances indexes. First, the IT2 Takagi-Sugeno fuzzy model and the controllers are constructed by considering the mismatched membership functions. Second, on the basis of Lyapunov stability theory, the IT2 fuzzy state and output feedback controllers are designed, respectively, to guarantee that the closed-loop system is asymptotically stable with extended dissipativity performance. The existence conditions of the two kinds of controllers are obtained in terms of convex optimization problems, which can be solved by standard software. Finally, simulation results are provided to illustrate the effectiveness of the proposed methods.

This paper is concerned with the stabilization problem for a class of discrete-time networked control systems (NCSs) with bounded time delays and packet losses. The controlled plant is represented by a Takagi–Sugeno fuzzy model, and both the state feedback control and output feedback control cases are considered. By guaranteeing the decrement of Lyapunov functional at each control signal updating step, a less conservative stability condition for the state feedback NCSs is derived, and the corresponding stabilizing controller design method is also presented. Under an observer-based framework, the output feedback stabilization problem is further studied, where the main contribution is the development of the separation principle for NCSs. Illustrative examples are provided to show the advantage and effectiveness of the developed results.

In this paper, the model approximation problem is investigated for a Takagi-Sugeno fuzzy switched system with stochastic disturbance. For a high-order considered system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with a Hankel-norm performance but translates it into a lower dimensional fuzzy switched system as well. By using the average dwell time approach and the piecewise Lyapunov function technique, a sufficient condition is first proposed to guarantee the mean-square exponential stability with a Hankel-norm error performance for the error system. The model approximation is then converted into a convex optimization problem by using a linearization procedure. Finally, simulations are provided to illustrate the effectiveness of the proposed theory.

This paper addresses the problems of dissipativity analysis for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying state delay and stochastic perturbation. Firstly, the uncertainty of state delay is pulled out of the original system by a novel model transformation process. Consequently the transformed model, which consists of a linear timeinvariant system and a norm-bounded uncertain subsystem, is obtained to simplify the dissipativity analysis. By using this model transformation method combined with the Lyapunov-Krasovskii technique, sufficient conditions of the dissipativity are established. Finally, two examples are presented: one shows the effectiveness of model transformation method, and the other presents comparisons with alternative approaches.

In this paper, the problem of reliable filter design with strict dissipativity has been investigated for a class of discrete-time T-S fuzzy time-delay systems. Our attention is focused on the design of a reliable filter to ensure a strictly dissipative performance for the filtering error system. Based on the reciprocally convex approach, firstly, a sufficient condition of reliable dissipativity analysis is proposed for T-S fuzzy systems with time-varying delays and sensor failures. Then, a reliable filter with strict dissipativity is designed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, numerical examples are provided to illustrate the effectiveness of the developed techniques.

This paper investigates the nonfragile-guaranteed cost control problem for a class of uncertain nonlinear singular state-delayed systems that is described by Takagi-Sugeno models. The upper bound of state delay is assumed to be available. A linear quadratic cost function is specified as the performance index of the closed-loop system. Attention is focused on the fuzzy state feedback controller design via parallel distributed compensation scheme, which not only guarantees the regular, impulse-free, and asymptotically stable of the closed-loop fuzzy singular delay system, but also provides an optimized upper bound of the guaranteed cost function for the possibility of feedback gain variation and all admissible uncertainties. All derived sufficient conditions are given in terms of linear matrix inequalities. Some numerical examples are provided to illustrate the effectiveness of the proposed design scheme.

This paper is concerned with the problems of D-stability and nonfragile control for a class of discrete-time descriptor Takagi-Sugeno (T-S) fuzzy systems with multiple state delays. D-stability criteria are proposed to ensure that all the poles of the descriptor T-S fuzzy system are located within a disk contained in the unit circle. Furthermore, a sufficient condition is presented such that the closed-loop system is regular, causal, and D-stable, in spite of parameter uncertainties and multiple state delays. The corresponding solvability conditions for the desired fuzzy-rule-dependent nonfragile controllers are also established. Finally, examples are given to show the effectiveness and advantages of the proposed techniques.

This paper is concerned with the problems of dissipativity analysis and synthesis for discrete-time Takagi-Sugeno fuzzy systems with stochastic perturbation and time-varying delay. First, a novel model transformation method is introduced to pull the time-varying delay uncertainty out of the original system. Consequently, the transformed model is composed of a linear time-invariant system and a norm-bounded uncertain subsystem. By using this model transformation method combined with the Lyapunov-Krasovskii technique, sufficient conditions of the dissipativity are established. Then, a fuzzy controller is designed to guarantee the dissipative performance of the closed-loop system. Finally, three examples are presented: one shows the effectiveness of model transformation method, the second performs the comparison with alternative approaches, and the third illustrates the applicability of the proposed dissipative control methods.

This paper is concerned with the problems of stability analysis and stabilization for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with stochastic perturbation and time-varying state delay. By means of the delay-partitioning method and slack variables, a novel fuzzy Lyapunov-Krasovskii function is constructed to reduce the conservatism of stability conditions. Those conditions are converted to finite linear matrix inequalities, which can be readily solved by standard numerical software. Then, the delay-dependent stabilization approach, which is based on a nonparallel distributed compensation scheme, is introduced for the closed-loop fuzzy systems. Finally, illustrative examples are provided to illustrate the feasibility and effectiveness of the proposed methods.

This paper is concerned with the problem of induced l2 filter design for a class of discrete-time Takagi-Sugeno fuzzy Itô stochastic systems with time-varying delays. Attention is focused on the design of the desired filter to guarantee an induced l2 performance for the filtering error system. A new comparison model is proposed by employing a new approximation for the time-varying delay state, and then, sufficient conditions for the obtained filtering error system are derived by this comparison model. A desired filter is constructed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper focuses on analyzing a new model transformation of discrete-time Takagi–Sugeno (T–S) fuzzy systems with time-varying delays and applying it to dynamic output feedback (DOF) controller design. A new comparison model is proposed by employing a new approximation for time-varying delay state, and then, a delay partitioning method is used to analyze the scaled small gain of this comparison model. A sufficient condition on discrete-time T–S fuzzy systems with time-varying delays, which guarantees the corresponding closed-loop system to be asymptotically stable and has an induced$\ell_{2}$disturbance attenuation performance, is derived by employing the scaled small-gain theorem. Then, the solvability condition for the induced$\ell_{2}$DOF control is also established, by which the DOF controller can be solved as linear matrix inequality optimization problems. Finally, examples are provided to illustrate the effectiveness of the proposed approaches.

The paper is concerned with the problem of distributed fuzzy filter design for a class of sensor networks described by discrete-time T-S fuzzy systems with time-varying delays and multiple probabilistic packet losses. In sensor network, each individual sensor can receive not only its own measurement but also its neighboring sensors' measurements according to the interconnection topology to estimate the system states. Our attention is focused on the design of distributed fuzzy filters to guarantee the filtering error dynamic system to be mean-square asymptotically stable with an average \mathscr H∞performance. Sufficient conditions for the obtained filtering error dynamic system are proposed by applying an comparison model and the scaled small gain theorem. Based on the measurements and estimates of the system states and its neighbors for each sensor, the solution of the parameters of the distributed fuzzy filters is characterized in terms of the feasibility of a convex optimization problem. Finally, an illustrative example is provided to illustrate the effectiveness of the proposed approaches in sensor networks.

This paper is concerned with the problems of exponential stability analysis and synchronization of discrete-time switched delayed neural networks. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with time-delays. Benefitting from the delay partitioning method and the free-weighting matrix technique, the conservatism of the obtained results is reduced. In addition, the decay estimates are explicitly given and the synchronization problem is solved. 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 paper, the problem ofl2-l∞filtering for a class of discrete-time Takagi-Sugeno (T-S) fuzzy time-varying delay systems is studied. Our attention is focused on the design of full- and reduced-order filters that guarantee the filtering error system to be asymptotically stable with a prescribedH∞performance. Sufficient conditions for the obtained filtering error system are proposed by applying an input-output approach and a two-term approximation method, which is employed to approximate the time-varying delay. The corresponding full- and reduced-order filter design is cast into a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper is concerned with the problem of H(∞) model reduction for Takagi-Sugeno (T-S) fuzzy stochastic systems. For a given mean-square stable T-S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an H(∞) performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.

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.

This paper is concerned with the problem of H∞model approximation for discrete-time Takagi-Sugeno (T-S) fuzzy time-delay systems. For a given stable T- S fuzzy system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well in an H∞performance but is also translated into a linear lower dimensional system. By applying the delay partitioning approach, a delay-dependent sufficient condition is proposed for the asymptotic stability with an H∞error performance for the error system. Then, the H∞model approximation problem is solved by using the projection approach, which casts the model approximation into a sequential minimization problem subject to linear matrix inequality (LMI) constraints by employing the cone complementary linearization algorithm. Moreover, by further extending the results, H∞model approximation with special structures is obtained, i.e., delay-free model and zero-order model. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.

This paper investigates the problems of stability analysis and stabilization for a class of discrete-time Takagi-Sugeno fuzzy systems with time-varying state delay. Based on a novel fuzzy Lyapunov-Krasovskii functional, a delay partitioning method has been developed for the delay-dependent stability analysis of fuzzy time-varying state delay systems. As a result of the novel idea of delay partitioning, the proposed stability condition is much less conservative than most of the existing results. A delay-dependent stabilization approach based on a nonparallel distributed compensation scheme is given for the closed-loop fuzzy systems. The proposed stability and stabilization conditions are formulated in the form of linear matrix inequalities (LMIs), which can be solved readily by using existing LMI optimization techniques. Finally, two illustrative examples are provided to demonstrate the effectiveness of the techniques proposed in this paper.

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.

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.

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.

The paper deals with the robust fault detection problem for Takagi–Sugeno (T--S) fuzzy ItÔ stochastic systems. Our aim is to develop a robust fault detection approach to the T--S fuzzy systems with Brownian motion. By using a general observer-based fault detection filter as a residual generator, the robust fault detection is formulated as a filtering problem. Attention is focused on the design of both the fuzzy-rule-independent and the fuzzy-rule-dependent fault detection filters guaranteeing a prescribed noise attenuation level in an${{\H}}_\infty$sense. Sufficient conditions are proposed to guarantee the mean-square asymptotic stability with an${{\H}}_\infty$performance for the fault detection system. The corresponding solvability conditions for the desired fuzzy-rule-independent and fuzzy-rule-dependent fault detection filters are also established. Finally, a numerical example is provided to illustrate the effectiveness of the proposed theory.

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.

This paper considers the general filtering problem for a distinct class of two-dimensional (2-D) discrete linear systems, i.e. information propagation in two independent directions, known as discrete linear repetitive processes which are of both system-theoretic and applications interest. In particular, new results on the design of filters with guaranteed levels of performance are developed. These take the form of algorithms for the design of an Hinfinand l2-linfindynamic output feedback filter which guarantees that the resulting filtering error process is stable and has prescribed disturbance attenuation performance as measured by Hinfinand l2-linfinnorms.