Conventional Model Predictive Control (MPC) often loses recursive feasibility when tasked with tracking aggressive or discontinuous dynamic reference trajectories. While existing Feasibility Governor (FG) frameworks effectively address this issue for steady-state setpoint regulation, they are often ill-suited for tracking continuously time-varying references, leading to conservative performance or tracking lag. To address this gap, this paper proposes a novel Dual-type Reference Governor (DRG) framework that seamlessly integrates with Tube-based MPC (TMPC). Unlike traditional governors, the DRG features a dual-mode architecture designed specifically for dynamic tracking: an Initial RG (IRG) handles large reference jumps to recover feasibility, while a Terminal RG (TRG) manages local trajectory refinement. Furthermore, to tackle the computational burden associated with robust control, a DRG-driven adaptive prediction horizon mechanism is introduced. This mechanism dynamically shortens the horizon during smooth tracking and extends it only when necessary for feasibility recovery. Theoretical analysis establishes the recursive feasibility and closed-loop stability of the proposed framework. Simulation studies and hardware experiments on a vehicle robot platform demonstrate that the DRG+TMPC significantly outperforms standard TMPC and conventional FG approaches in terms of tracking accuracy for dynamic targets and computational efficiency.

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.

This article presents a new design of self-triggered impulsive control (STIC) for prescribed-time stability (PTS) of nonlinear systems. A prescribed-time convergent function is proposed to determine the impulsive control strength together with the information of the impulsive interval. Different from previous impulsive control strategies, the impulsive control with designable strength is implemented under a non-Zeno self-triggering mechanism (STM) with enforced bounded execution times and the triggering times established by a comparison system. The involvement of the upper bound of the impulsive interval in both control strength and STM not only renders the closed-loop prescribed-time stable but also ensures decreasing impulsive strengths as the time approaches the prescribed time. The validity of the proposed STIC scheme is verified by the application to prescribed-time synchronization of reaction-diffusion neural networks (RDNNs) and numerical examples.

Magnetic resonance imaging (MRI)-guided focused ultrasound (MRg-FUS) is an effectivenoninvasiveintervention. However, when extending to mild hyperthermia treatment (mHT), accurate temperature control with uniform thermal distribution remains challenging for deep-seated targets in highly heterogeneous tissues. To this end, we propose a novel thermal modeling andclosed-loopcontrol scheme for robot-assisted MRg-FUS mHT. A discrete-time positive system is introduced for thermal dynamics modeling. By introducing an event-triggered model predictive controller, objective functions are formulated to optimize transducerphasesequences. Thermal feedback is leveraged to accommodate modeling uncertainties,closingthe control loop to generate constructive ultrasound interference. Our scheme enables simultaneous spatial and temporal heating control, maintaining target temperature within a narrow range ($41\,^\circ \mathrm{C}\text{--}43\,^\circ \mathrm{C}$). The thermal dose evaluated under unknown disturbances demonstrates the robustness of the proposed scheme against severe tissue heterogeneity. Overheating can be avoided, enhancing the potential for safe intervention. Results from mechanical transducer adjustment further support the feasibility of treating large target areas in robot-assisted mHT.

Robust flight in complex and windy environments is critical for both single and multiple quadrotors. Existing methods either learn disturbance model at high computational cost or use error-based adaptive control with a speed-stability trade-off that makes tuning difficult. To address these issues, this paper proposes a future-trend-aware filter-based PD-MRAC (Proportional-Derivative Model Reference Adaptive Control) for single quadrotor and a distributed PD-MRAC for multiple quadrotor formation. By embedding a trend-aware derivative term in the adaptive update laws, the controller obtains anticipatory information about the error evolution, enabling rapid adaptation while mitigating oscillations. For more disturbance-sensitive multi-quadrotors, we design a robust distributed protocol under a directed graph, improving resilience to disturbances. The approach maintains low computational cost and supports fast adaptive updates. Extensive simulations and real-world experiments validate improvement. For single quadrotor, RMSE reduced by around 57% versus the baselines and by around 12% versus the DJI Mavic 2. For multi-quadrotors, formation results show enhanced robustness in simulation and effective real-world indoor/outdoor experiments under strong winds. Our project page is athttps://xiongtao-shi.github.io/PD-MRAC/.

In this work, a novel dual-type reference governor (DRG) is proposed to handle the unreachable reference within the prediction horizon for the model predictive control (MPC). The proposed method, featuring two types of reference governor (RG) designs, ensures efficient reference tracking and MPC feasibility throughout the entire process. The first type, the initial RG, is specifically designed to adjust references when the controller receives a new target reference. This addresses a gap in existing research on command/feasibility governors (FGs), which overlooks this scenario. The second type, named terminal RG, ensures the convergence of auxiliary references to the current target reference. By introducing control variables, it expands the feasible set of auxiliary references compared to existing methods, thereby further accelerating the convergence speed. The recursive feasibility, finite-time convergence, and asymptotic stability of the combined DRG+MPC closed-loop system with the proposed algorithm are demonstrated in the article. To validate the effectiveness of the proposed algorithm, numerical simulations were conducted in two vehicle application scenarios: adaptive cruise control (ACC) and lane changing control (LCC), using their respective vehicle models. The results indicate that, compared to existing methods such as classical MPC, tracking MPC (TMPC), and FG+MPC, the proposed approach guarantees both reference tracking performance and low computational burden.

For pneumatic artificial muscles, it is always considered the more maximum contraction ratio the better. While for human joint assisting applications, PAMs with configurable maximum contraction rate are more suitable because of advantageous safety and adaptability. A PAM based on planar-to-specific-wave body shape morph is proposed in this work. Shape-morphing-based braided artificial muscles (SBAMs) have uniqueness of initial elasticity and maximum contraction ration programmability, which meet the favors of human joint assisting applications. The basic structure and working mechanism of contraction in SBAMs will be explained, and their mathematical model will also be established. According to the experimental results, a SBAM prototype generates a force more than 140 times its weight under an easily accessible pressure of 150 kPa. A mannequin wearing the SBAM enables actively flexes its elbow over 120 °.

This article explores a novel secure synchronization scheme for drive-response Lur’e systems under denial-of-service (DoS) attacks. The main innovative features are characterized by an aperiodic transmission mechanism without attack detection, a resilient sampled-data control linked with timer, and a highly aperiodic-interval-dependent Lyapunov functional. The output error of the drive-response system is aperiodically sampled and transmitted through networks subject to DoS attacks. By applying a timer, this aperiodic transmission mechanism assesses whether the aperiodic sampled-data characteristic is impacted by attacks at any time, thus avoiding the need for attack detection and enhancing its practicality. Correspondingly, a resilient sampled-data control linked with timer is developed, wherein the control signal dynamically changes when not impacted by attacks, improving secure control performance for achieving synchronization. Additionally, a new secure synchronization standard compatible with this scheme is introduced. Based on the framework, secure synchronization analysis and control results are derived through the proposed highly aperiodic-interval-dependent Lyapunov functional. More specially, the matrix variables in this functional are explicitly related to each aperiodic sampling transmission interval, as well as those used for handling nonlinear functions, which contributes to reduced conservativeness in the results. The theoretical advantages of these new methods and results are compared with existing literature. Finally, the novel secure synchronization scheme is applied to two examples to validate the effectiveness and superiority, including a 3-neuron neural network and a chaotic Chua’s circuit.

This paper studies the prescribed-time tracking control problem for nonlinear systems with unknown time-varying parameters, mismatched nonvanishing uncertainties, unknown control coefficients, and potential actuator faults. The proposed control strategy employs a prescribed-time adjustment function to guarantee that state errors converge to zero within a specified time, despite the presence of nonvanishing mismatched uncertainties. The proposed controller avoids the need to use adaptive mechanisms and is therefore simple to implement. Moreover, the proposed control strategy does not require the control coefficient bounds to be known. Based on a prescribed-time scaling function and a barrier function, prescribed performance for state errors is guaranteed, which is uniform with respect to initial conditions, eliminating the need for an offline optimization algorithm to determine the controller gains. The simulation results demonstrate the effectiveness of the proposed control framework.

This article presents a distributed pinning control strategy aimed at achieving global stabilization of Markovian jump Boolean control networks. The strategy relies on network matrix information to choose controlled nodes and adopts the algebraic state space representation approach for designing pinning controllers. Initially, a sufficient criterion is established to verify the global stability of a given Markovian jump Boolean network (MJBN) with probability one at a specific state within finite time. To stabilize an unstable MJBN at a predetermined state, the selection of pinned nodes involves removing the minimal number of entries, ensuring that the network matrix transforms into a strictly lower (or upper) triangular form. For each pinned node, two types of state feedback controllers are developed: 1) mode-dependent and 2) mode-independent, with a focus on designing a minimally updating controller. The choice of controller type is determined by the feasibility condition of the mode-dependent pinning controller, which is articulated through the solvability of matrix equations. Finally, the theoretical results are illustrated by studying the T cell large granular lymphocyte survival signaling network consisting of 54 genes and 6 stimuli.

This article considers the finite-frequency (FF) $H_{2}$ - $H_{\infty }$ composite control problem for continuous singularly perturbed systems. To address the performance requirements in the low- and high-frequency ranges, the FF $H_{2}$ and $H_{\infty }$ norms are used to impose on the performance of the slow and fast subsystems, respectively. The FF $H_{2}$ control of the slow subsystem is analyzed using the FF Gramian matrix method. While the FF $H_{\infty }$ control of the fast subsystem is studied by using the Generalized Kalman-Yakubovič-Popov Lemma. Subsequently, an $H_{2}$ - $H_{\infty }$ composite controller for the singularly perturbed system is developed. Finally, two simulation examples involving an armature control direct-current motor system are demonstrated to verify the effectiveness and superiority of the proposed control scheme.

This article is concerned with the problem of asynchronous control for Interval Type-2 (IT2) fuzzy nonhomogeneous Markov jump systems against successive denial-of-service (DoS) attacks. The system and the controller are assumed to be connected through a communication channel subject to malicious attacks. The maximum number and probability distribution of successive attacks are considered. Under the imperfect premise matching, a fuzzy asynchronous controller is constructed by the hidden Markov model. By means of the introduced transmission delay, a delay closed-loop system is constructed, where the stochastic description of the delay depends on the statistical characteristic of successive attacks. Then stability criteria together are derived in the form of linear matrix inequalities by the Lyapunov functional approach, as well as the condition on the existence of the fuzzy controller. Finally, the feasibility and effectiveness of the presented control scheme are demonstrated by simulation results.

The control of a humanoid robot equipped with one arm and multiple fingers, designed primarily for grasping and manipulating various objects, is investigated. Synchronizing the movements of the fingers is a challenging task, as each joint must reach the desired angle simultaneously to ensure a firm grasp. The success of this task hinges on the synchronization of convergence times for each finger joint; otherwise, the object may slip or escape. This challenge is further intensified by uncertainties in the dynamics of the hand or the object. We present decentralized prescribed-time tracking control strategies for the dynamical system comprising the arm-finger combination. In this system, the fingers are primarily used for grasping the object while the arm is responsible for moving, tilting, or flipping it. To streamline the controller structure and simplify the stability analysis, we design a linear controller based on the maximum eigenvalue of a parameter matrix and establish a new technical lemma, which paves the way for the stability analysis of the prescribed-time tracking and the reduction of the input efforts of the actuator. We develop robust and decentralized adaptive control schemes separately for the arm and fingers, achieving better transient performance with less prior knowledge and lower computation costs. Finally, we validate the proposed controller's performance through kinematic simulations of grasping and moving tasks in 3-D, alongside numerical simulations that demonstrate the tracking performance of our algorithm in the joint space.

The passivity-based asynchronous control is tackled for 2-D Roesser Markovian jump systems (MJSs) and stabilization is guaranteed when 2-D MJSs are susceptible to Denial-of-Service (DoS) attacks. A novel jump model is proposed in this article, where the switching law of subsystems is regulated by the sum of the horizontal and vertical coordinates' values. This differs from the conventional jump model, which presumes that the transition probabilities are identical in both directions. The proposed jump model can avoid the mode ambiguity problem. Given the openness and sharing nature of communication networks, they are susceptible to malicious cyber-attacks that impair system performance. The concept of global time is introduced to help characterize the jump law and construct DoS attack model. Besides, a hidden Markov model (HMM) is utilized to manage the inevitable mismatched mode problem induced by any delay or data dropouts. With the above considerations, several conditions are established for ensuring passivity performance of 2-D MJSs and stabilization when facing DoS attacks. Several equivalent solvable conditions are derived via decoupling strategy and matrix inequality technique. Finally, two simulation examples are provided to demonstrate the validity of the established theoretical results.

This paper presents a novel event-triggered robust distributed model predictive control (DMPC) methodology with a shrinking prediction horizon and shrinking terminal region for vehicle platoon control in the presence of external disturbances and communication delays. Using the framework of tube-based MPC, an information transmission triggering mechanism is designed which is determined by control plan changes for the nominal leading vehicle, to reduce the communication frequency. Then the follower vehicles only update their control inputs, prediction horizons and corresponding terminal sets when they receive new information from the leader, so as to reduce the computational burden. The proposed approach effectively compensates the information errors caused by the communication delays by utilizing the timestamp information. When the leader’s control plan changes during the delay period, the recursive feasibility of the local optimization problem and the uniform ultimate bound stability of the platoon system are guaranteed with the proposed controller. The numerical simulation demonstrates the superior performance and robustness of the proposed approach, which offers promising solutions for real-world platoon control systems.

Soft pneumatic actuators (SPAs) are widely used in robotic applications due to their inherent compliance and outstanding mechanical performance. However, a tradeoff between load capacity and deformation capacity is required when selecting material hardness for SPAs. Too hard material usually results in limited deformation, such as small extension, bending, and twisting, while too soft a material decreases robustness. This study introduces coil-reinforced flat tube actuators (CFTAs) that exhibit excellent flexibility and high load capacity by braiding flat tubes in coil springs. By adjusting the braiding pattern of the flat tube, the CFTAs can realize extending, in-plane bending, and out-of-plane helical bending motions. In addition, analytical models are proposed to predict the deformation behavior of the CFTAs and verified by experiments. The bending type CFTA deforms with an excellent curvature (0.516 mm−1) under 160 kPa input pressure, five times larger than the reported SPAs on the same scale. The CFTAs show high design flexibility by programming the flat tube pattern and using multiple coiled springs for wide application scenarios. Based on the CFTAs, this study shows wearable upper limb robots, a soft entanglement gripper, and a rob-climbing robot. CFTAs provide design insight for applications requiring dexterous and versatile deformations.

With the vast demand in marine development, robotic fish show promising potential in underwater exploration for their high-performance propulsion ability. However, fish-inspired robots are yet to utilize the structural flexibility of rhythmic actuation such as bony fish (Osteichthyes). The Body and Caudal Fin (BCF) locomotion in fish optimizes the use of muscle power and body flexibility by synchronizing muscle activation with the undulating-oscillatory tail-flapping, such as Thunniform, while robotic fish are primarily designed as motion trackers rather than as efficient swimmers. In this paper, we propose a power allocation strategy (PAS) that imitates muscle rhythmic actuation, which increases the flapping amplitude by the coupling of the peduncle motion and the tail deformation. Inspired by this peduncle-tail mechanism, we developed a Direct-Drive Fish Robot (DDRFishBot). The DDRFishBot is enhanced by our developed PAS in Tail-Elastic Potential Energy (T-EPE) release by 228%, in propulsion by 45.6% and in efficiency coefficient by 16.3%. This study establishes the performance enhancement principle of exploiting tail flexibility through a simple scotch yoke mechanism, expanding the performance space of fish-inspired tail-flapping swimming robot.

This article introduces an innovative approach to tackle the consensus issue for positive multiagent systems (MASs) with input nonlinearities. By analyzing a differential equation model of MASs with sector-bounded nonlinearity, positivity conditions are derived. Furthermore, a distributed state-feedback control protocol is applied to achieve consensus and simultaneously to preserve positivity. An iterative algorithm is used to compute a consensus controller gain. The saturation nonlinearity and its approximated form are also considered. Finally, the effectiveness of proposed method is verified through numerical examples. The findings of this study can potentially contribute to developing control strategies for MASs with nonlinear input dynamics.

This work addresses the output reachable set-based leader-following consensus problem, focusing on a group of positive agents over directed dwell-time switching networks. Two types of non-negative disturbances, namely, 1)$L_{1}$-norm bounded disturbances and 2)$L_{\infty,1}$-norm bounded disturbances are studied. Meanwhile, a class of directed dwell-time switching networks for modeling the communication protocol of positive agents is investigated. To deal with the presence of disturbances, an output-feedback control protocol is developed to achieve a robust consensus with positivity preserved based on the output reachable set. By exploiting the positive characteristics, switched linear copositive Lyapunov functions are adopted to establish output reachable set-based consensus conditions. These conditions can facilitate the control protocol design by solving a bilinear programming problem, and also generate hyperpyramidal regions to confine the output consensus error. A particle swarm optimization-based (PSO-based) algorithm is applied to compute the controller gains and optimize the volume of the hyperpyramids. The proposed methods are verified by the presented numerical case studies.

This research paper addresses the problem of achieving differentially private average consensus for multiagent systems (MASs) consisting of positive agents. A novel randomized mechanism is introduced that employs nondecaying positive multiplicative truncated Gaussian noises to maintain the positivity and randomness of the state information over time. A time-varying controller is developed to achieve mean-square positive average consensus, and convergence accuracy is evaluated. The proposed mechanism is shown to preserve (ϵ,δ) -differential privacy of MASs, and the privacy budget is derived. Numerical examples are provided to illustrate the effectiveness of the proposed controller and privacy mechanism.

This article deals with the load frequency control problem of multiarea power systems subject to multiple intermittent denial-of-service (DoS) attacks that can interrupt the data transmission of each area independently. A decentralized event-triggering (ET) scheme under periodic sampling is proposed to reduce the transmission burden. Then, a decentralized ET-based controller is designed by tuning several parameters. Such control scheme can reduce the computational complexity while achieving privacy preserving. Moreover, sufficient conditions are derived for ensuring the input-to-state stability of multiarea power systems and an optimization solution method via particle swarm algorithm is provided. Finally, a three-area power system is employed to verify the effectiveness of the proposed scheme.

This work investigates the control design and reachable set estimation problem for T-S fuzzy systems under bounded external disturbances. By designing a fuzzy controller, the reachable set of the closed-loop fuzzy system can be bounded by an ellipsoid that is minimized via two optimization algorithms. When the fuzzy control scheme is implemented under an event-triggered framework, the limited information availability may degrade desired control performance due to only the triggered state being available for the fuzzy controller. To alleviate this problem, a novel reachable-set-dependent event-triggering condition is proposed such that satisfactory control performance can still be ensured by the above fuzzy controller. Meanwhile, the bounding ellipsoid of the reachable set is explicitly given, and a relationship is established between control performance and communication cost under the event-triggered scheme. In addition, under the condition that the reachable set remains within a specified safe area, the communication rate can be reduced while still achieving optimal control performance. The proposed strategies are verified via a simulation example.

This article studies the fault-tolerant consensus problem with the guaranteed transient performance of multiagent systems (MASs) subject to unknown time-varying actuator faults and disturbances. The general actuator faults, including both multiplicative and additive time-varying faults, are considered in such a problem for the first time. Both single-integrator modeled agents and double-integrator modeled agents are investigated. The transient performance is ensured in the sense that position errors between each pair of neighboring agents are guaranteed within certain user-defined time-varying performance bounds. Adaptive laws are designed to estimate information about faults and disturbances. For MASs with additive faults, the proposed controllers ensure errors asymptotically converge to zero with guaranteed transient performance. For MASs with both multiplicative faults and additive faults, the proposed controllers ensure errors converge to a residual set without asymptotic convergence but still with guaranteed transient performance. Two simulation examples are provided to evaluate the proposed schemes.

state-feedback control problem for networked discrete-time systems with positivity constraint. This problem (for a single positive system), raised recently in the area of positive systems theory, is known to be challenging due to its inherent nonconvexity. In contrast to most works, which only provide sufficient synthesis conditions for a single positive system, we study this problem within a primal-dual scheme, in which necessary and sufficient synthesis conditions are proposed for networked positive systems. Based on the equivalent conditions, we develop a primal-dual iterative algorithm for solution, which helps prevent from converging to a local minimum. In the simulation, two illustrative examples are employed for verification of our proposed results.

This article is concerned with the sliding mode control (SMC) problem for a class of Markov jump systems subject to packet dropouts, in which the dropped or received status of packet is described by a Markov chain. To enhance the reliability of data transmission, multiple redundant channels are employed between sensors and the controller. Different from the existing measurement model under redundant channels with Bernoulli-process-based packet dropouts, a novel measurement model under Markov-chain-based packet dropouts is proposed under the redundant channel transmission. It is assumed that the modes of the controlled system and packet-dropout model are unavailable, and then a mode detection mechanism is proposed to detect the partially unavailable modes. By utilizing the detected modes, a dynamic observer is constructed to estimate the unmeasurable system state, based on which a detected-mode-dependent sliding mode controller is designed to achieve the mean-square exponential ultimate boundedness of the closed-loop system. Meanwhile, incremental search technique and particle swarm optimization algorithm are, respectively, utilized to solve two optimization problems for enhancing the closed-loop performances. Finally, two simulation examples are provided to verify the effectiveness of the proposed schemes.

This work addresses the problem of reachable set-based consensus for positive multiagent systems affected by typical classes of bounded disturbances. In the presence of disturbances, a reachable set-based consensus with positivity preservation is proposed to ensure that the state of the closed-loop system remains positive while enclosing the reachable set of the defined consensus error with an ellipsoidal bounding region. Sufficient conditions are established to achieve the reachable set-based positive consensus under the energy-bounded or peak-bounded disturbance input. Equivalent design conditions are provided, for which a heuristic algorithm is proposed for computing and optimizing the bounding region. Simulations are conducted to validate the obtained results.

The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model’s robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://github.com/JiarenX/AGAIN.

In this article, we focus on the state estimation problems for a system with protecting user privacy. Regarding whether the user has conducted a sensitive action in the system as a kind of privacy, we propose a privacy-preserving mechanism (PPM) to prevent its action results from being disclosed or inferred. For such a system with the PPM, we first obtain the optimal estimator (OE). Subject to the inoperability of the OE in practice, we turn to designing a computationally efficient suboptimal estimator (SE) as an alternative. Then, we prove that this SE can remain stable while satisfying the user's requirements on both privacy protection and estimation performance. By solving a privacy-preserving optimization problem, a set of guidelines is established to customize a tradeoff between privacy and performance according to the user's demand. Finally, illustrated examples are used to illustrate the main theoretical results.

In this work, the security sliding mode control problem is considered for interval type-2 (IT2) fuzzy systems, where the sensor-controller channel may be subject to the false data injection attack with multi-strategy attack model. The different attack patterns at each instant is described via a random categorical distribution. It is noted that the injected false data may result in the system state to exceed the modeling domain. In order to cope with the above phenomenon, a reconstruction method of the controller's membership functions is proposed via analyzing the variation range of the attacked state, and then, an IT2 fuzzy sliding mode controller is designed. The explicit relation between the raw state signal and attacked state is established, based on which stability conditions dependent on the regional boundaries of membership functions are obtained for the input-to-state stability in probability of the closed-loop system. Meanwhile, a framework of security performance optimization is established via searching a desirable sliding matrix, which may be solved via the particle swarm optimization algorithm. Furthermore, the proposed security control scheme is extended to the case of imperfectly matched premises, where the controller may have different fuzzy rules from the fuzzy system. Finally, the proposed security control scheme is verified by two simulations examples.

This paper proposes a switched velocity-dependent path-following control method for autonomous ground vehicles under uncertain cornering stiffness and time-varying velocity. A switched polytopic linear-parameter-varying (LPV) model combining path-following and vehicle lateral dynamics is established, where the velocity-dependent parameters are divided into several switched regions. In each region, a trapezoidal polytope is adopted to describe the varying parameters. A global$H_{\infty }$performance analysis is carried out for the switched polytopic LPV vehicle system based on the average dwell time approach with multiple Lyapunov functions. Based on the analysis, a switched velocity-dependent controller is synthesized to achieve a desired path-following performance by regulating the steering angle of the front wheels. The proposed switched path-following control specializes in handling large range of varying velocity, and achieves superior path-following performance compared with the conventional gain-scheduling control. Simulations are carried out for typical road maneuvers to illustrate the advantages of the proposed method. The results are also applicable to other LPV analysis and synthesis conditions involving time-varying parameters of large varying ranges.

This article investigates the positive consensus problem of a special kind of interconnected positive systems over directed graphs. They are composed of multiple fractional-order continuous-time positive linear systems. Unlike most existing works in the literature, we study this problem for the first time, in which the communication topology of agents is described by a directed graph containing a spanning tree. This is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains, which renders the positivity analysis fairly challenging. Based on the existing results in spectral graph theory, fractional-order systems (FOSs) theory, and positive systems theory, we derive several necessary and/or sufficient conditions on the positive consensus of fractional-order multiagent systems (PCFMAS). It is shown that the protocol, which is designed for a specific graph, can solve the positive consensus problem of agents over an additional set of directed graphs. Finally, a comprehensive comparison study of different approaches is carried out, which shows that the proposed approaches have advantages over the existing ones.

The robust filtering problem is tackled for a class of shift-varying two-dimensional systems with uncertain-variance noises under the scheduling of the weighted try-once-discard (WTOD) protocol. The measurements collected from the sensors are transmitted to a remote filter via a shared network. To alleviate the communication burden and obviate the network congestion, the WTOD protocol is adopted to orchestrate the data transmission, where a sensor node is solely permitted to broadcast its information to the remote filter at every transmission step. Moreover, the resilient filter is exploited to regulate the possible gain perturbation. The objective of the addressed problem is to design a robust filter in a recursive structure such that, in the simultaneous presence of the uncertain-variance noises and the WTOD protocol, the minimal upper bounds (UBs) on the filtering error variances (EVs) are developed for the considered system. First, by means of induction and stochastic analysis technique, certain UBs in terms of coupled recursive difference equations are derived for the actual EVs. Then, a proper filter is carefully designed which achieves the minimization of the obtained UBs at each step. Finally, an illustrative example is presented to verify the usefulness of the proposed protocol-based filtering method.

This article focuses on the event-triggered sliding mode control for interval type-2 (IT2) fuzzy systems subject to the randomly occurring denial-of-service (DoS) attacks. Under the resource-limited and unreliable network, a probability-dependent adaptive event-triggered scheme (AETS) is proposed, whose triggering parameter can be adaptively adjusted in accordance with the convergence trend of the system state and the attack probability. By constructing a sliding function with one-step-memorized state information, a fuzzy sliding mode resilient controller is designed, whose membership functions (MFs) dependent on the compromised triggered state do not require the same fuzzy rules as the fuzzy system. Furthermore, by employing the global bounds of mismatched MFs, the co-design conditions for the AETS and the fuzzy controller are derived for attaining both the reachability and the stability. Finally, a simulation example and the comparison results are provided.

In this article, the non-negative edge consensus problem is addressed for positive networked systems with undirected graphs using state-feedback protocols. In contrast to existing results, the major contributions of this work included: 1) significantly improved criteria of consequentiality and non-negativity, therefore leading to a linear programming approach and 2) necessary and sufficient criteria giving rise to a semidefinite programming approach. Specifically, an improved upper bound is given for the maximum eigenvalue of the Laplacian matrix and the (out-) in-degree of the degree matrix, and an improved consensuability and non-negativevity condition is obtained. The sufficient condition presented only requires the number of edges of a nodal network without the connection topology. Also, with the introduction of slack matrix variables, two equivalent conditions of consensuability and non-negativevity are obtained. In the conditions, the system matrices, controller gain, as well as Lyapunov matrices are separated, which is helpful for parameterization. Based on the results, a semidefinite programming algorithm for the controller is readily developed. Finally, a comprehensive analytical and numerical comparison of three illustrative examples is conducted to show that the proposed results are less conservative than the existing work.

Differential privacy, which has been widely applied in industries, is a privacy mechanism effective in preventing malicious entities from breaching the privacy of an individual participant. It is usually achieved by adding random variables in the data. This article investigates a class of multivariable discrete-time multiagent systems with ϵ -differential privacy preserved. A novel information-masking mechanism is proposed, in which the information of each state transmitted to different neighbors is obscured by adding independent random noises. Then, the mean-square consensus conditions, and the upper bound and lower bound of the convergence rate are obtained. Moreover, the conditions for the convergence rate reaching its upper bound are established. The results can be applied to the average mean-square consensus. In addition, a necessary and sufficient condition is presented under which agents can preserve the dynamics of agents ϵ -differentially private at any time instant.

The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic deception attacks modeled by two independent Markov processes. Two discrete event-triggered mechanisms are introduced for both channels to reduce the number of data transmission through the communication channels. To comply with practical point of view, static output feedback is utilized. By employing the Lyapunov-Krasovskii functional method, some sufficient conditions on the synchronization of master-slave neural networks are derived in terms of linear matrix inequalities, which make it easy to design suitable output feedback controllers. Finally, a numerical example is presented to show the effectiveness of the proposed method.

This article addresses the distributed consensus problem for identical continuous-time positive linear systems with state-feedback control. Existing works of such a problem mainly focus on the case where the networked communication topologies are of either undirected and incomplete graphs or strongly connected directed graphs. On the other hand, in this work, the communication topologies of the networked system are described by directed graphs each containing a spanning tree, which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Specifically, the problem involves complex eigenvalues, the Hurwitzness of complex matrices, and positivity constraints, which make analysis difficult in the Laplacian matrix. First, a necessary and sufficient condition for the consensus analysis of directed networked systems with positivity constraints is given, by using positive systems theory and graph theory. Unlike the general Riccati design methods that involve solving an algebraic Riccati equation (ARE), a condition represented by an algebraic Riccati inequality (ARI) is obtained for the existence of a solution. Subsequently, an equivalent condition, which corresponds to the consensus design condition, is derived, and a semidefinite programming algorithm is developed. It is shown that, when a protocol is solved by the algorithm for the networked system on a specific communication graph, there exists a set of graphs such that the positive consensus problem can be solved as well.

This article investigates the nonnegative consensus tracking problem for networked systems with a distributed static output-feedback (SOF) control protocol. The distributed SOF controller design for networked systems presents a more challenging issue compared with the distributed state-feedback controller design. The agents are described by multi-input multi-output (MIMO) positive dynamic systems which may contain uncertain parameters, and the interconnection among the followers is modeled using an undirected connected communication graph. By employing positive systems theory, a series of necessary and sufficient conditions governing the consensus of the nominal, as well as uncertain, networked positive systems, is developed. Semidefinite programming consensus design approaches are proposed for the convergence rate optimization of MIMO agents. In addition, by exploiting the positivity characteristic of the systems, a linear-programming-based design approach is also proposed for the convergence rate optimization of single-input multi-output (SIMO) agents. The proposed approaches and the corresponding theoretical results are validated by case studies.

This article is focused on the design of an output tracking control scheme for a class of continuous-time periodic piecewise time-varying systems (PPTVSs) with actuator saturation and nonlinear perturbations. The energy-to-peak tracking performance is studied based on an equivalent condition on the definiteness property of matrix polynomials. Considering the actuator saturation and nonlinear perturbation, matrix polynomial-based sufficient conditions are derived through the Lyapunov method using periodic matrix functions. From a perspective of subinterval segmentation aimed at PPTVSs, the proposed conditions can achieve less conservatism for tracking the output of a periodic time-varying reference system, while the controller gains can be computed using convex optimization. Moreover, a heuristic algorithm is constructed to simultaneously guarantee the closed-loop state convergence and the output tracking performance. The reduction in conservatism and the effectiveness of algorithm are demonstrated by illustrative case studies.

This paper investigates the problems of reachable set estimation and synthesis for periodic positive systems with two different exogenous disturbances. The lifting method and the pseudoperiodic Lyapunov function method are adopted for the estimation problem. The reachable set bounding conditions are proposed by employing Lyapunov-based inequalities and the S-procedure technique. Two optimization methods are used to minimize the bounding hyper-pyramids of the reachable set. In addition, the state-feedback controller design conditions that make the reachable set of closed-loop systems lie within a given hyper-pyramid are derived. Finally, numerical examples are presented to illustrate the validity of the obtained conditions.

This article investigates the hybrid sliding mode control problem for the uncertain Markovian jump systems (MJSs) via the transition rates optimal design. The stability condition for the transition rates is first established to ensure the exponential mean-square (EMS) stability of the unforced uncertain MJSs. Then the hybrid design strategy on the sliding mode controller and transition rate matrix is presented to ensure the EMS stability of the controlled system. Moreover, the iterative optimization algorithms are developed to acquire the desirable transition rates, control gain, and decay rate$\sigma $. Finally, some numerical simulation results are provided.

This article considers the problem of designing lead, lag, and lead-lag compensators for multi-input-multi-output (MIMO) linear systems under the H∞performance measure. This is the first time that the lead, lag and lead-lag compensators are generalized to the MIMO cases by preserving their classical compensator structures. Theoretical results on the stability analysis and synthesis of MIMO systems under the H∞control performance with the proposed lead, lag, and lead-lag compensators are obtained. Then relevant algorithms for designing lead, lag, and lead-lag compensators are provided to determine the compensator parameters. Differing from traditional design methods, which mostly rely on some trial-and-error procedures, the proposed methods are algorithmic and the compensators can be synthesized systematically. Illustrative examples are used to demonstrate the effectiveness and advantages of the proposed methods.

This paper investigates the positive edge consensus problems for undirected networks, where each edge is modeled by a continuous-time positive system with sector input nonlinearities. Based on a given nodal network, the corresponding edge network is presented, and some relationships between the given nodal network and its edge network are given. The observer-based protocol for non-negative consensus is proposed. Rigorous theoretical analysis which considers both the input nonlinearities and the positivity constraint is introduced to achieve a global consensus result. Simultaneous stabilization problems are constructed for edge-based non-negative consensus using the maximum and the minimum nonzero eigenvalues of the edge Laplacian matrix. Furthermore, the robustness of the positive edge consensus to parameter perturbation is illustrated. Sufficient non-negative consensus conditions related to the bounds of system matrices are given, and the non-negative edge consensus conditions for star nodal networks are also given only using the vertex number. Iterative linear matrix inequalities (LMIs)/LMI algorithm is presented to obtain the feedback matrix and the observer matrix. The given theoretical results are finally illustrated by some simulations.

In this article, we take the coordination control problem of linear time-invariant networked systems with uncertain additive disturbance and uncertain initial states into consideration. A distributed interval observer is first constructed for uncertain networked systems in which the control algorithm of each agent involves only the upper bound information and the lower bound information of the interval observer associated with itself and its neighbors, respectively. With the help of the cooperativity theory, it is proved that the interval observer can estimate the piecewise state for each agent and the interval-observer-based control algorithm can drive the uncertain system to achieve coordination behavior. Then, time-varying coordinate transformation is introduced to construct a novel interval observer which can eliminate the cooperativity premise on the system matrices and bound the states of all agents in real time. It is shown that the novel interval-observer-based control algorithm can guide the uncertain system to reach coordinated behavior. Finally, the numerical simulations are provided to verify the theoretical results.

This paper proposes a novel remote tracking control strategy for a class of discrete-time Takagi-Sugeno fuzzy systems with randomly occurring uncertainties and randomly varying local nonlinear models. The outputs of the fuzzy system are collected through an unreliable sensor subject to missing measurements. Simultaneously, the outputs of the remote models are transmitted to the controller through wireless channels, in which the fading measurements may inevitably happen. By considering the Rice fading model and the Markovian packet dropouts model, an output-feedback controller is designed such that the closed-loop fuzzy tracking system is robustly stochastically stable and a prescribed H∞remote tracking performance is achieved. Furthermore, sufficient conditions are obtained for the existence of admissible tracking controllers in terms of nonstrict linear matrix inequalities. To overcome the difficulty in computation, a modified cone-complementarity linearization algorithm is employed to cast the tracking controller design problem into a sequential minimization one, which can be readily solved by using standard numerical software. Simulation results demonstrate the effectiveness of the developed control algorithm for fuzzy remote tracking controller.

This paper investigates stability and guaranteed cost of time-triggered Boolean networks (BNs) based on the semitensor product of matrices. The time triggering is generated by mode-dependent average dwell-time switching signals in the BNs. With the help of the copositive Lyapunov function, a sufficient condition is derived to ensure that the considered network is globally stable under a designed average dwell-time switching signal. Subsequently, an infinite time cost function is further discussed and its bound is presented according to the obtained stability result. Numerical examples are finally given to show the feasibility of the theoretical results.

In this paper, a novel monitoring approach is developed for nonlinear processes based on available measurements. To cope with complicated process nonlinearity, we implement total projection to latent structures (T-PLS) in each local model created by the locally weighted projection regression (LWPR) algorithm. Under the framework of locally weighted learning, four improved test statistics are established to detect potential process faults. The test statistics are not only capable to monitor the abnormal changes in the relevant subspaces, but also with thresholds suitable for non-Gaussian measurements. The effectiveness of our proposed approach is further demonstrated by a numerical nonlinear case.

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

In this paper, we study the problem of finite-time consensus of multiagent systems on a fixed directed interaction graph with a new protocol. Existing finite-time consensus protocols can be divided into two types: 1) continuous and 2) discontinuous, which were studied separately in the past. In this paper, we deal with both continuous and discontinuous protocols simultaneously, and design a centralized switching consensus protocol such that the finite-time consensus can be realized in a fast speed. The switching protocol depends on the range of the initial disagreement of the agents, for which we derive an exact bound to indicate at what time a continuous or a discontinuous protocol should be selected to use. Finally, we provide two numerical examples to illustrate the superiority of the proposed protocol and design 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 revisits the problem of asymptotic stability analysis for neural networks with distributed delays. The distributed delays are assumed to be constant and prescribed. Since a positive-definite quadratic functional does not necessarily require all the involved symmetric matrices to be positive definite, it is important for constructing relaxed Lyapunov-Krasovskii functionals, which generally lead to less conservative stability criteria. Based on this fact and using two kinds of integral inequalities, a new delay-dependent condition is obtained, which ensures that the distributed delay neural network under consideration is globally asymptotically stable. This stability criterion is then improved by applying the delay partitioning technique. Two numerical examples are provided to demonstrate the advantage of the presented stability criteria.

This paper is concerned with the 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.

An approach of control design using fuzzy estimators (FEs) is proposed for quantum systems with uncertainties. Two types of quantum control problems are considered: 1) control of a pure-state quantum system in the presence of uncertainties and 2) control design of quantum systems with initial mixed states and uncertainties. For the first type of tasks, a partial feedback control scheme with an FE is presented to design controllers. In this scheme, an FE is trained to estimate the quantum state for feedback control of a quantum system, and controlled projective measurement is used to assist in controlling the system. For the second type of quantum control tasks, a probabilistic fuzzy estimator (PFE) is trained to estimate the quantum state for control design of a quantum system with an initial mixed state, and a corresponding control algorithm is proposed to design a control law that drives the system from the mixed state to a target pure state. Two examples of two-spin-1/2systems are also presented and analyzed to demonstrate the process of control design and potential applications of the proposed approach.

This paper is concerned with the problems of stability and dissipativity analysis for static neural networks (NNs) with time delay. Some improved delay-dependent stability criteria are established for static NNs with time-varying or time-invariant delay using the delay partitioning technique. Based on these criteria, several delay-dependent sufficient conditions are given to guarantee the dissipativity of static NNs with time delay. All the given results in this paper are not only dependent upon the time delay but also upon the number of delay partitions. Some examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.

This paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi-Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

Noise disturbances and time delays are frequently met in cellular genetic regulatory systems. This paper is concerned with the disturbance analysis of a class of genetic regulatory networks described by nonlinear differential equation models. The mechanisms of genetic regulatory networks to amplify (attenuate) external disturbance are explored, and a simple measure of the amplification (attenuation) level is developed from a nonlinear robust control point of view. It should be noted that the conditions used to measure the disturbance level are delay-independent or delay-dependent, and are expressed within the framework of linear matrix inequalities, which can be characterized as convex optimization, and computed by the interior-point algorithm easily. Finally, by the proposed method, a numerical example is provided to illustrate how to measure the attenuation of proteins in the presence of external disturbances.

In this brief, the problems of delay-dependent stability analysis and strict (Q,S,ℜ)-α-dissipativity analysis are investigated for cellular neural networks (CNNs) with distributed delay. First, by introducing an integral partitioning technique, two new forms of Lyapunov-Krasovskii functionals are constructed, and improved distributed delay-dependent stability conditions are established in terms of linear matrix inequalities. Based on this criterion, a new sufficient delay and α-dependent condition is given to guarantee that the CNNs with distributed delay are strictly (Q,S,ℜ)-α-dissipative. The results developed in this brief can tolerate larger allowable delay than existing ones in the literature, which is demonstrated by several examples.

This paper is concerned with the transient and steady-state estimates of a class of genetic regulatory networks (GRNs). Some sufficient conditions, which do not only present the transient estimate but also provide the estimates of decay rate and decay coefficient of the GRN with interval parameter uncertainties (interval GRN), are established by means of linear matrix inequality (LMI) and Lyapunov-Krasovskii functional. Moreover, the steady-state estimate of the proposed GRN model is also investigated. Furthermore, it is well known that gene regulation is an intrinsically noisy process due to intracellular and extracellular noise perturbations and environmental fluctuations. Then, by utilizing stochastic differential equation theory, the obtained results are extended to the case with noise perturbations due to natural random fluctuations. All the conditions are expressed within the framework of LMIs, which can easily be computed by using standard numerical software. A three-gene network is provided to illustrate the effectiveness of the theoretical results.

This paper is concerned with the problem ofHinfinfuzzy filtering of nonlinear systems with intermittent measurements. The nonlinear plant is represented by a Takagi-Sugeno (T-S) fuzzy model. The measurements transmission from the plant to the filter is assumed to be imperfect, and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the missing measurements. Attention is focused on the design of anHinfinfilter such that the filter error system is stochastically stable and preserves a guaranteedHinfinperformance. A basis-dependent Lyapunov function approach is developed to design theHinfinfilter. By introducing some slack matrix variables, the coupling between the Lyapunov matrix and the system matrices is eliminated, which greatly facilitates the filter-design procedure. The developed theoretical results are in the form of linear matrix inequalities (LMIs). Finally, an illustrative example is provided to show the effectiveness of the proposed approach.

This paper proposes a new approach, namely, the delay partitioning approach, to solving the problems of stability analysis and stabilization for continuous time-delay Takagi-Sugeno fuzzy systems. Based on the idea of delay fractioning, a new method is proposed for the delay-dependent stability analysis of fuzzy time-delay systems. Due to the instrumental idea of delay partitioning, the proposed stability condition is much less conservative than most of the existing results. The conservatism reduction becomes more obvious with the partitioning getting thinner. Based on this, the problem of stabilization via the so-called parallel distributed compensation scheme is also solved. Both the stability and stabilization results are further extended to time-delay fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities (LMIs), which can be readily solved via standard numerical software. The advantage of the results proposed in this paper lies in their reduced conservatism, as shown via detailed illustrative examples. The idea of delay partitioning is well demonstrated to be efficient for conservatism reduction and could be extended to solving other problems related to fuzzy delay systems.

This paper investigates the problem of fault detection for Takagi-Sugeno (T-S) fuzzy systems with intermittent measurements. The communication links between the plant and the fault detection filter are assumed to be imperfect (i.e., data packet dropouts occur intermittently, which appear typically in a network environment), and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the unreliable communication links. The aim is to design a fuzzy fault detection filter such that, for all data missing conditions, the residual system is stochastically stable and preserves a guaranteed performance. The problem is solved through a basis-dependent Lyapunov function method, which is less conservative than the quadratic approach. The results are also extended to T--S fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities, which can be readily solved via standard numerical software. Two examples are provided to illustrate the usefulness and applicability of the developed theoretical results.

This paper is concerned with the stability analysis of discrete-time recurrent neural networks (RNNs) with time delays as random variables drawn from some probability distribution. By introducing the variation probability of the time delay, a common delayed discrete-time RNN system is transformed into one with stochastic parameters. Improved conditions for the mean square stability of these systems are obtained by employing new Lyapunov functions and novel techniques are used to achieve delay dependence. The merit of the proposed conditions lies in its reduced conservatism, which is made possible by considering not only the range of the time delays, but also the variation probability distribution. A numerical example is provided to show the advantages of the proposed conditions.

This paper is concerned with the problems of stability analysis and stabilization for discrete-time Takagi-Sugeno fuzzy systems with time-varying state delay. By constructing a new fuzzy Lyapunov function and by making use of novel techniques, an improved delay-dependent stability condition is obtained, which is dependent on the lower and upper delay bounds. The merit of the proposed stability condition lies in its reduced conservatism, which is achieved by avoiding the utilization of some bounding inequalities for the cross products between two vectors. Then, a delay-dependent stabilization approach based on a parallel distributed compensation scheme is developed for both state feedback and observer-based output feedback cases. The proposed stability and stabilization conditions are formulated in terms of linear matrix inequalities, which can be solved efficiently by using existing optimization techniques. Two illustrative examples are provided to demonstrate the effectiveness of the results proposed in this paper.

Motivated by the study of a class of networked control systems, this correspondence paper is concerned with the design problem of stabilization controllers for linear systems with stochastic input delays. Different from the common assumptions on time delays, it is assumed here that the probability distribution of the delay taking values in some intervals is knowna priori. By making full use of the information concerning the probability distribution of the delays, criteria for the stochastic stability and stabilization controller design are derived. Traditionally, in the case that the variation range of the time delay is available, the maximum allowable bound of time delays can be calculated to ensure the stability of the time-delay system. It is shown, via numerical examples, that such a maximum allowable bound could be made larger in the case that the probability distribution of the time delay is known.

In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.

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.

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

This paper addresses the problem of stability for two-dimensional systems with delays in the state. To solve this problem, the Lyapunov second method is used. The resulting condition is written in terms of linear matrix inequalities and it is dependent on the size of delays. This fact allows us to reduce the conservatism in the stability analysis of two-dimensional systems with state delays. A simulation example is given to illustrate the theoretical developments.

This work investigates the problem of robust output feedback H/sub /spl infin// control for a class of uncertain discrete-time fuzzy systems with time delays. The state-space Takagi-Sugeno fuzzy model with time delays and norm-bounded parameter uncertainties is adopted. The purpose is the design of a full-order fuzzy dynamic output feedback controller which ensures the robust asymptotic stability of the closed-loop system and guarantees an H/sub /spl infin// norm bound constraint on disturbance attenuation for all admissible uncertainties. In terms of linear matrix inequalities (LMIs), a sufficient condition for the solvability of this problem is presented. Explicit expressions of a desired output feedback controller are proposed when the given LMIs are feasible. The effectiveness and the applicability of the proposed design approach are demonstrated by applying this to the problem of robust H/sub /spl infin// control for a class of uncertain nonlinear discrete delay systems.

This paper considers the problem of robust exponential epsilon-regulation for a class of multi-input nonlinear systems with uncertainties. The uncertainties appear not only in the feedback channel but also in the control channel. Under some mild assumptions, an adaptive neural network control scheme is developed such that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded and, under the control scheme with initial data starting in some compact set, the states of the closed-loop system is guaranteed to exponentially converge to an arbitrarily specified epsilon-neighborhood about the origin. The important contributions of the present work are that a new exponential uniformly ultimately bounded performance is proposed and that the design parameters and initial condition set can be determined easily. The development generalizes and improves earlier results for the single-input case.

This paper considers the problem of global robust stability analysis of delayed cellular neural networks (DCNNs) with norm-bounded parameter uncertainties. In terms of a linear matrix inequality, a new sufficient condition ensuring a nominal DCNN to have a unique equilibrium point which is globally asymptotically stable is proposed. This condition is shown to be a generalization and improvement over some previous criteria. Based on the stability result, a robust stability condition is developed, which contains an existing robust stability result as a special case. An example is provided to demonstrate the reduced conservativeness of the proposed results.

This paper is concerned with the problem of stabilizing controller design for 2-D systems with Markovian jump parameters. The mathematical model of 2D jump systems is established upon the well-known Roesser model, and sufficient conditions are obtained for the existence of desired controllers in terms of linear matrix inequalities (LMIs), which can be readily solved by available numerical software. A numerical example is provided to show the applicability of the proposed theories.

This paper deals with the problem of non-fragile H/sub /spl infin// control for neutral systems with time-varying delays. The state feedback gains to be designed are subject to norm-bounded uncertainties. The purpose of the problem is the design of a state feedback controller such that the resulting closed-loop system is asymptotically stable while satisfying a prescribed H/sub /spl infin// performance level for all admissible uncertainties in the controller gain. Sufficient conditions for the solvability of this problem are obtained for the cases with additive and multiplicative controller uncertainties, respectively. A desired non-fragile state feedback controller can be obtained by solving certain linear matrix inequalities.

This paper considers the problem of robust stabilization for stochastic time-delay systems with convex polytopic uncertainties. Sufficient conditions for the solvability of the problem are obtained by using parameter-independent and parameter-dependent Lyapunov functional, respectively. It is shown that the result derived by a parameter-dependent Lyapunov functional is less conservative. A desired state feedback controller can be designed by solving a set of linear matrix inequalities.

In this letter, we study the robust filtering problem for linear uncertain discrete time-delay systems with Markovian jump parameters. The system under consideration is subjected to time-varying norm-bounded parameter uncertainties, time-delay in the state, and Markovian jump parameters in all system matrices. A filter is designed to guarantee that the dynamics of the estimation error is robustly stochastically stable in the mean square, irrespective of the admissible uncertainties as well as the time-delay. It is shown that the problem addressed can be solved in terms of the solutions to a set of coupled matrix Riccati-like inequalities.

In this paper, we consider the H/sub 2/ sampled-data control for uncertain linear systems by the impulse response interpretation of the H/sub 2/ norm. Two H/sub 2/ measures for sampled-data systems are considered. The robust optimal control procedures subject to these two H/sub 2/ criteria are proposed. The development is primarily concerned with a multirate treatment in which a periodic time-varying robust optimal control for uncertain linear systems is presented. To facilitate multirate control design, a new result of stability of hybrid system is established. Moreover, the single-rate case is also obtained as a special case. The sampling period is explicitly involved in the result which is superior to traditional methods. The solution procedures proposed in this paper are formulated as an optimization problem subject to linear matrix inequalities. Finally, we present a numerical example to demonstrate the proposed techniques.

The paper is concerned with simultaneous linear-quadratic (LQ) optimal control design for a set of LTI systems via piecewise constant output feedback. First, the discrete-time simultaneous LQ optimal control design problem is reduced to solving a set of coupled matrix inequalities and an iterative LMI algorithm is presented to compute the feedback gain. Then, simultaneous stabilization and simultaneous LQ optimal control design of a set of LTI continuous-time systems are considered via periodic piecewise constant feedback gain. It is shown that the design of a periodic piecewise constant feedback gain simultaneously minimizing a set of given continuous-time performance indexes can be reduced to that of a constant feedback gain minimizing a set of equivalent discrete-time performance indexes. Explicit formulas for computing the equivalent discrete-time systems and performance indexes are derived. Examples are used to demonstrate the effectiveness of the proposed method.