Search

In this paper, the problem of sampled-data adaptive tracking control for a class of switched nonlinear systems with prescribed performance is considered. In order to guarantee the system is stable and achieves the prescribed performance in sampled-data control, a coordinate transformation satisfying the prescribed performance is introduced. In addition, the neural networks (NNs) used to approximate the unknown nonlinear functions and the backstepping technique are applied to design the sampled-data controller and the adaptive laws. An upper bound on the sampling period is obtained to maintain the stability of the systems. It is confirmed that the designed sampled-data scheme ensures that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error is limited to the prescribed performance function. The effectiveness of the designed control scheme is demonstrated by two simulation examples. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimating reachable set in leaderless consensus for multi-agent systems with Lipschitz nonlinear dynamics and bounded external disturbances. Initially, a sampled-data control is introduced to address the consensus of nonlinear multi-agent systems vulnerable to deception attacks and packet dropouts, which occur randomly during sampling intervals. Then, aperiodic sampling in various degrees is taken into account in the primary Lyapunov term. Sufficient conditions to guarantee that all the actual states of the multi-agent, starting from the initial state, can be bounded within a given ellipsoid set are established by designing a suitable controller. Moreover, the consensus control design is established as linear matrix inequalities, utilizing a two-sided looped functional and Wirtinger's inequality-based discontinuous Lyapunov–Krasovskii functional. Finally, the numerical section validates the applicability of the proposed control method. [ABSTRACT FROM AUTHOR]

This article investigates the distributed optimization feedback control for a family of multi‐agent systems with external disturbances. For physical implementation in the scenario that only the sampled‐state information is available, a novel disturbance compensation distributed optimization control strategy is proposed by designing a sampled‐data‐based distributed protocol and a sampled‐data‐based disturbance compensator. The disturbances in the current sampling interval are compensated by an exact value at the time in the last sampling interval obtained by using the sampled data. It is proved that the states of agents converge to an arbitrarily small domain of the optimal point of the global cost function if the disturbances and their derivatives are bounded, and the sampling period is short enough. Besides, when disturbances are constants, all the agents' states converge to the optimal point asymptotically. Simulations consolidated the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Sampled‐data control problems via memoryless state and output feedback are studied, respectively, for a class of strongly nonlinear systems with state and input delays. Under sample and hold, both state and output feedback control schemes are developed by virtue of the emulation method, the adding a power integrator technique, and the recursive design of nonlinear observers. With the aid of Lyapunov–Krasovskii functional theorem, together with the idea of robust control, we prove that the proposed sampled‐data state and output feedback controllers make the hybrid closed‐loop systems with delays and uncertainty globally asymptotically stable, if the input delay and sampling period are limited. The class of time‐delay uncertain systems under consideration goes beyond the Lipschitz or linear growth condition and is genuinely nonlinear in the sense that it contains uncontrollable/unobservable linearization and is not stabilizable, even locally, by any linear or smooth feedback. Applications of the sampled‐data robust control schemes presented in this article are illustrated by examples with simulations. [ABSTRACT FROM AUTHOR]

The issue of non-fragile sampled-data control for synchronizing Markov jump Lur'e systems (MJLSs) with time-variant delay was investigated. The time-variant delay allowed for uncertainty that was constrained to an interval with defined upper and lower boundaries. The components of the nonlinear function within the MJLSs were considered to satisfy either Lipschitz continuity or non-decreasing monotonicity. Numerically tractable conditions that ensured stochastic synchronization with a predefined L 2 − L ∞ disturbance attenuation level for the drive-response MJLSs were established, utilizing time-dependent two-sided loop Lyapunov-Krasovskii functionals, together with integral and matrix inequalities. An exact mathematical expression of the desired controller gains can be obtained based on these conditions. Finally, an example with numerical simulation was employed to demonstrate the effectiveness of the proposed control strategies. [ABSTRACT FROM AUTHOR]

In this study, our main objective is to address the issue of sampled‐data‐based synchronization of complex networks subjected to stochastic scaling attacks using a looped‐functional approach. To begin with, the design of a Lyapunov functional, which involves tuning parameters and slack variables, considers the error state information of χ(tk)$$ \chi \left({t}_k\right) $$ and χ(tk+1)$$ \chi \left({t}_{k+1}\right) $$. Then, the scaling attack is a type of deception attack that is formulated using an interval matrix approach. In addition, the complex networks considered in this study incorporate uncertain inner coupling strengths, which are more general than the existing ones, owing to the presence of uncertainties in the coupling strengths. Using a looped‐functional approach to construct an appropriate Lyapunov–Krasovskii functional, we derive a set of sufficient conditions to achieve mean‐square asymptotic synchronization for considered complex networks under scaling attacks. In the end, the proposed results are illustrated using three examples, including a practical example that highlights their effectiveness and superiority. [ABSTRACT FROM AUTHOR]

This article realizes an adaptive finite‐time sampled‐data output‐feedback stabilization for a class of fractional‐order nonlinear systems with unmodeled dynamics and unavailable states. K‐filters are constructed to estimate unavailable states, a dynamic signal is introduced to handle unmodeled dynamics and neural networks were used to approximate uncertain nonlinearities existed in stabilizer construction. With the help of backstepping technique, an adaptive sampled‐data output‐feedback stabilizer is exported, and such stabilizer with allowable design parameters and sampling period can render the corresponding closed‐loop system reaches practically finite‐time stable, which can be demonstrated by means of selected Lyapunov function candidates. In the end, two simulations with a numerical and an engineering examples are presented to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates adaptive sampled-data control for strict-feedback nonlinear plants with unmatched uncertainties by means of radial basis function neural networks (RBFNNs). First, the continuous-time plant is locally discretized as a disturbed strict-feedback model by using the approximate Euler model approach. Then, as a basis of rigorous stability analysis, the concept of validity is proposed, which, considering the locality of the universal approximation capacity in RBFNNs, requires that the argument of each RBFNN be inside the corresponding compact set all the time. Meanwhile, to address the noncausality issue, delayed signals are utilized in the backstepping method for discrete-time plants. Subsequently, the validity and stability are proved rigorously; meanwhile, a practical output tracking problem is solved under a time-varying reference signal, the order of whose continuous derivatives is the same as the plants. This is the first time the interdependence on the design of sampling periods and RBFNNs in different design steps has been shown. Finally, simulation results are provided to illustrate the efficiency and feasibility of the obtained results. [ABSTRACT FROM AUTHOR]

Lyapunov-based approach to sampled-data set stabilisation of constrained delayed Boolean control networks (DBCNs) is investigated in this paper. The main mathematical tool is semi-tensor product (STP) of matrices. Since the sampling interval is selected from a finite set, the STP method is adopted to convert the dynamics of constrained DBCNs under nonuniform sampled-data control into a switched Boolean network (SBN). It is worth noting that the switches can only occur at the sampling instant. Using the techniques of Lyapunov function and average dwell time, several sufficient conditions are proposed for the global stability of SBN. Moreover, by virtue of reachable set approach, a procedure is established to design state feedback sampled-data stabilisers for constrained DBCNs. The obtained results are applied to the cell survival regulation of apoptosis networks. [ABSTRACT FROM AUTHOR]

This paper focuses on event‐triggered sampled‐data synchronization of uncertain complex networks with time‐varying coupled delays. First of all, a discrete event‐triggered sampled‐data control scheme is adopted, which not only makes the state of the system be monitored in discrete time, but also the sampling information is effectively transmitted. The proposed event‐triggered mechanism effectively prevents Zeno behavior. In addition, we also use some novel piecewise time‐dependent Lyapunov–Krasovskii and Wirtinger inequality to handle the time‐varying delays and parameter uncertainties of complex networks. Then, synchronization criteria are given for uncertain complex networks. Finally, the simulation results show that the control scheme can significantly reduce the number of transmitted signals while maintaining the uncertain complex networks synchronization. [ABSTRACT FROM AUTHOR]

The dissipativity‐based analysis and synthesis issues of Takagi‐Sugeno (T‐S) fuzzy systems by applying aperiodic memory sampling control are investigated in this paper. Compared with the more conservative dissipative conditions and controller design methods proposed in the past based on Lyapunov theory, the main purpose of this paper is to obtain a less conservative dissipative criterion and devise an aperiodic memory sampled‐data controller. Firstly, the sampling controller is assumed to work in aperiodic sample mode, and the time delay in the communication network is considered. Next, an augmented delay‐product‐type looped‐functional is proposed such that the whole information of sampling and delay, which has not been studied before, is well taken into account. A new dissipative criterion and an aperiodic sampled‐data control strategy are proposed by applying free‐matrix‐based integral inequalities consequently. Finally, a truck‐trailer system is implemented to elaborate the availability and the merit of the designed strategy. [ABSTRACT FROM AUTHOR]

The sampling data control of bidirectional associative memory (BAM) neural network with leakage delay is considered in this article. The BAM model is viewed as a mixed delay that combines a distributed delay, a discrete delay that varies over time, and a delay in the leaking period. The sampling system is then converted to a continuous time-delay system using an input delay method. In order to get adequate conditions in the form of linear matrix inequalities(LMIs), we build a new Lyapunov-Krasovskii Functional (LKF) in conjunction with the free weight matrix approach. Finally, a simulation results are given to show the efficiency of the theoretical approach. [ABSTRACT FROM AUTHOR]

This paper investigates the chaotic synchronization of neural networks via sampled-data control with actuator saturation. First, a new generalized looped-functional is constructed by considering valid information from $t_{k}$ to t and from t to $t_{k+1}$ . Then, local stability conditions for the synchronization error system are given based on the Lyapunov stability theory. A sampled-data controller is designed to achieve the synchronization of the driving neural network and the response neural network by solving the convex optimization problem with given conditions. The proposed method’s effectiveness and practicality are numerically verified.

Inspired by the cyber-physical systems (CPS) of numerical methods for stochastic differential equations, we present a CPS model of sampled-data control systems (typically a synonym for computer control systems), which regards the intersection of the physical and the cyber (the key feature of CPS). As a theoretic foundation, we develop by the Lyapunov method a stability theory for a general class of stochastic impulsive differential equations (SiDE) which is formulated as a canonical form for CPS that may work in feedback loops and thus include those of sampled-data control systems. Applying the fundamental theory, we study stability of the CPS, which implies that of the sampled-data control system. By our CPS approach, we not only obtain stability criteria for the CPS of sampled-data control systems but also reveal the equivalence and intrinsic relationship between the two main approaches (viz. controller emulation and discrete-time approximation) in the literature. As the applications of our CPS theory, we propose a control design method for feedback stabilization of the CPS of sampled-data stochastic systems. Illustrative examples are conducted to verify that our method significantly improves the existing results. In this paper, we initiate the study of a systems science of design for CPS. This provokes many open and interesting problems.

To ensure comfort and longevity of vehicle components, it is important to suppress low-frequency transient vibrations in the powertrain. This study proposes a unique technique for active vibration control that incorporates six rules-based fuzzy logic compensation. The technique addresses changes in control periods of an actuator over time. Firstly, a model prediction technique is used with a sampled-data controller (SDC) to address the highest possible phase delay in the control input caused by the fluctuating control period. In addition, fuzzy sets are utilized to define the changing renewal timings of the control input, which are distinct from the regular timings used by the periodically operated SDC. These fuzzy sets are named “Nearly previous timing” and “Nearly upcoming timing”. This study employs six inference rules to achieve fuzzy compensation that resembles human intuition. These rules utilize output variables defined by linguistic fuzzy sets, such as “Similar to commands from SDC” and “Very similar to commands from SDC”, making the inference process more flexible. Due to the utilization of the fuzzy sets and periodic control signals provided by the SDC, it is possible to reasonably deduce unknown control inputs at various update timings. To evaluate the effectiveness of the control scheme, simulations and experiments are conducted using an actual test setup to investigate its damping performance. The experimental findings indicate that the new active damping technique effectively minimizes transient powertrain vibrations. Furthermore, comparative studies with previous control systems indicate the improved performance and robustness of the proposed approach.

In this study, the stability and stabilization analyses are discussed for Takagi-Sugeno (T-S) fuzzy systems with input saturation. A fuzzy-based sampled-data control is designed to stabilize the T-S fuzzy systems. Based on the Lyapunov method and some integral inequality techniques, a set of sufficient conditions is obtained as linear matrix inequality (LMI) constraints to guarantee the asymptotic stability of the considered system. In this process, the linear switching method is utilized to design a controller that is dependent on the membership function, and an integral inequality is utilized. Additionally, determination of the controller parameters is achieved by resolving a series of LMI constraints. The effectiveness of these criteria is demonstrated through a real system that is modeled by the T-S system.

This paper investigates the sampled-data stabilization of continuous-time probabilistic logical control networks (CT-PLCNs). CT-PLCNs can provide quantitative and accurate descriptions for the transient kinetics in comparing discrete-time probabilistic logical control networks (DT-PLCNs). First, CT-PLCNs are transformed into switched continuous-time probabilistic logical networks by regarding the control input as a switching signal. In this setup, CT-PLCNs can be classified into two types: one with stable modes and the other with only unstable modes. Then the concept of average l-sample dwell time is proposed to describe the scenario, where the dwell time of each mode is an integral multiple of the sampling period l. Based on this, the stabilization conditions for CT-PLCNs are established by restricting the sampling dwell time of controller modes. Furthermore, a copositive Lyapunov function is constructed for the case with stable modes and is discretized for the case without stable modes, providing a new framework for studying the stabilization of CT-PLCNs. Finally, a chemical model generated by GINsim is provided to demonstrate the feasibility of the obtained theoretical results. Overall, this paper provides new insights into the stabilization of CT-PLCNs and presents practical applications for chemical models. [ABSTRACT FROM AUTHOR]

This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique. [ABSTRACT FROM AUTHOR]

We introduce the theoretical framework from geometric optimal control for a control system modeled by the generalized Lotka–Volterra equation, motivated by restoring the gut microbiota infected by Clostridium difficile combining antibiotic treatment and fecal injection. We consider both permanent control and sampled‐data control related to the medical protocols. [ABSTRACT FROM AUTHOR]

In this paper, the problem of state estimation for a fractional‐order neural networks system with uncertainties is studied by a sampled‐data controller. First, considering the convenience of digital field, such as anti‐interference, not affected by noise, a novel sampled‐data controller is designed for the fractional‐order neural network system of uncertainties with changeable sampling time. In the light of the input delay approach, the sampled‐data control system of fractional‐order is simulated by the delay system. The main purpose of the presented method is to obtain a sampled‐data controller gain K$$ K $$ to estimate the state of neurons, which can guarantee the asymptotic stability of the closed‐loop fractional‐order system. Then, the fractional‐order Razumishin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. Improved delay‐dependent and order‐dependent stability conditions are given in the form of LMIs. Furthermore, the sampled‐data controller can be acquired to promise the stability and stabilization for fractional‐order system. Finally, two numerical examples are proposed to demonstrate the effectiveness and advantages of the provided method. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of sampled‐data practical tracking for Euler‐Lagrange systems subject to uncertain parameters. We assume that the system matrix of the exosystem and the Euler‐Lagrange system both contain unknown parameters, which is clearly more practical. The existence of unknown system parameters invalidates existing control strategies and poses a major challenge to the solvability of the problem. With the help of the internal model principle and the adaptive control technology, we propose a novel sampled‐data dynamic compensator to overcome the challenge of unknown parameters in exosystems. In particular, a virtual sampling adaptive control input is proposed to deal with uncertainties in the exosystem matrix. Then, a sampled‐data control law is constructed to guarantee that, by any fast predefined rate, the tracking error exponentially approaches any small predefined ranges of the origin, thus ensure the tracking performance. Finally, we apply our result to a three‐link robot manipulator system. [ABSTRACT FROM AUTHOR]

This paper proposes a sampled-data fuzzy controller design technique for an autonomous underwater vehicle (AUV) depth system represented by an interval type-2 (IT-2) fuzzy model, considering input saturation. In the Takagi–Sugeno (T–S) fuzzy model of an AUV depth system, surge velocity is chosen as a premise variable. To address the associated uncertainty with this variable, we employ the IT-2 fuzzy modeling technique. Also, the controller proposed in this paper utilizes time-varying gains, ensuring superior exponential stability compared with traditional fixed gain approaches. Furthermore, a membership function-dependent (MFD) H ∞ criterion is employed to enhance robustness for each subsystem individually. Taking into account the mentioned aspects, the controller design condition is derived in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is validated through simulation examples. [ABSTRACT FROM AUTHOR]

Sampled-data control of PDEs has become an active research area; however, existing results are confined to deterministic PDEs. Sampled-data controller design of stochastic PDEs is a challenging open problem. In this paper we suggest a solution to this problem for 1D stochastic diffusion-reaction equations under discrete-time nonlocal measurement via the modal decomposition method, where both the considered system and the measurement are subject to nonlinear multiplicative noise. We present two methods: a direct one with sampled-data controller implemented via zero-order hold device, and a dynamic-extension-based one with sampled-data controller implemented via a generalized hold device. For both methods, we provide mean-square L² exponential stability analysis of the full-order closed-loop system. We construct a Lyapunov functional V that depends on both the deterministic and stochastic parts of the finite-dimensional part of the closedloop system. We employ corresponding Ito's formulas for stochastic ODEs and PDEs, respectively, and further combine V with Halanay's inequality with respect to the expected value of V to compensate for sampling in the infinite-dimensional tail. We provide linear matrix inequalities (LMIs) for finding the observer dimension and upper bounds on sampling intervals and noise intensities that preserve the mean-square exponential stability. We prove that the LMIs are always feasible for large enough observer dimension and small enough bounds on sampling intervals and noise intensities. A numerical example demonstrates the efficiency of our methods. The example shows that for the same bounds on noise intensities, the dynamic-extension-based controller allows larger sampling intervals, but this is due to its complexity (generalized hold device for sample-data implementation compared to zero-order hold for the direct method). [ABSTRACT FROM AUTHOR]

This article discusses the stability problem of sampled-data-based dynamic positioning ships (DPSs) using Takagi–Sugeno (T-S) fuzzy models. Firstly, dynamic equations for sampled-data DPSs are established. Simultaneously combining several symmetric matrices with new integral terms, a novel Lyapunov–Krasovskii function (LKF) is constructed, which allows the information of a sampling pattern to be fully captured. Next, via the constructed LKF, the positive definiteness requirements of a LKF are further relaxed, and the conservatism of the result can be reduced. Consequently, stability criteria are given, and fuzzy sampled-data controllers are designed in terms of linear matrix inequality (LMI). Finally, a simulation example is provided to verify the superiority and applicability of the developed methods. [ABSTRACT FROM AUTHOR]

The asymptotic stability of the fractional-order neural networks system with uncertainty by sampled-data controller is addressed in the article. First, considering the influence of uncertainty and fractional-order on the system, a novel sampled-data controller is designed with alterable sampling period. In the light of the input delay approach, the fractional system is simulated by the delay system. The main purpose of the method presented is to design a sampled-data controller, which the closed-loop fractional-order system can guarantee the asymptotic stability. Then, the fractional-order Razumikhin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. The stability conditions are presented in the form of LMIs on the novel delay-dependent and order-dependent. Furthermore, the sampling controller can be acquired to promise the stability and stabilization for fractional-order system. Finally, two numerical examples are proposed to demonstrate the effectiveness and advantages for the provided method. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we discuss the security control problem of cyber physical systems based on identifying false data injection attacks. First, the unknown adversary randomly attacks the measurement output, a mechanism is set in the sensor to update the measurement output to the constant, called the one‐valued quantized mechanism. Then, the Bienayme–Chebyshev inequality and the one‐valued quantized mechanism are utilized to design an identification algorithm, the estimated value of the false data injection attack and the minimum identification time can be obtained. Furthermore, based on the LMI theory, sampled‐data control gains with and without attacks are obtained to ensure the system mean‐square exponential stable, respectively. And the corresponding maximum sampling period of the controller is obtained. Finally, numerical examples verify the correctness and effectiveness of the theory. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the real power sharing and frequency regulation of distributed generators in islanded microgrids with abnormal asynchronous stochastic cyber attacks, which is of great significance to the information security and stable operation of microgrids. Firstly, considering the possible cyber attacks in the communication network, a distributed non-fragile controller with coupled memory delay is proposed according to the nonperiodic sampled-data control. Then, the construction of delay-dependent two-sided looped-functional makes the Lyapunov–Krasovskii functional contain more delay and sampling information and relaxes constraints on free matrices. In addition, based on the enhanced integral inequality technique and the linear convex combination method, a sampling-based consensus protocol is presented to solve the issues of real power sharing and frequency regulation in the islanded microgrids. Finally, to verify the effectiveness and feasibility of the designed control strategy, a modified IEEE 34-bus test system is used for the experiment. • This paper studies the real power sharing and frequency regulation issues in islanded microgrids and fully considers a class of stochastic cyber attacks which are abnormal and asynchronous with the system state. In addition, a new secondary control model considering such cyber attacks is established, which facilitates the study of the security performance of microgrids in complex and variable network environments. • Considering the possible disturbance of the controller in the physical platform and the delay of information transmission, a distributed non-fragile controller with coupled memory delay is designed to be as close to the real situation as possible. • A new delay-dependent two-sided looped-functional is introduced to make the Lyapunov–Krasovskii functional contain more delay and sampling information and reduce constraints on free matrices. Based on the linear convex combination and aperiodic sampled-data method, we can save more network resources and reduce the sampling data. [ABSTRACT FROM AUTHOR]

In this paper we provide a Pontryagin maximum principle for optimal sampled-data control problems with nonsmooth Mayer cost function. Our investigation leads us to consider, in a first place, a general issue on convex sets separation. Precisely, thanks to the classical Fan's minimax theorem, we establish the existence of a universal separating vector which belongs to the convex envelope of a given set of separating vectors of the singletons of a given compact convex set. This so-called universal separating vector lemma is used, together with packages of convex control perturbations, to derive a Pontryagin maximum principle for optimal sampled-data control problems with nonsmooth Mayer cost function. As an illustrative application of our main result we solve a simple example by implementing an indirect numerical method. [ABSTRACT FROM AUTHOR]

This paper investigates the local stability and stabilization criteria of sampled-data control systems, taking into account actuator saturation and peak-bounded exogenous disturbances. Specifically, this study introduces two innovations to extend the maximum upper bound of the sampling interval: two novel time integrals of the weighted state derivative are introduced to formulate an improved looped-functional; second, the introduction of two supplementary zero-equalities to improve the relationship among the components of the augmented state. Building on this, a set of linear matrix inequality-based stabilization conditions is derived. These conditions ensure that a closed-loop sampled-data system can become exponentially stable and achieve a guaranteed peak-to-peak performance in the domain of attraction. Finally, the efficacy of the proposed methodology is substantiated through both simulation and experimental results, focusing on the sampled-data control of an inverted pendulum system. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of stability and stabilization for Takagi–Sugeno (T–S) fuzzy systems with adaptive event-triggered scheme (AETS) based on sampled-data control. AETS is used to relieve network congestion and save bandwidth resources. A novel Lyapunov–Krosovskii functional (LKF) is proposed by introducing the available information of fuzzy membership functions (FMFs) and sampling instant. The FMFs approach, extended reciprocal convex inequality technique and some slack matrices are fully utilized to deal with the derivative of the LKF. Then, an improved criterion with less conservatism is obtained to guarantee the stability of T–S fuzzy system. Moreover, the standard conditions are given in the form of linear matrix inequalities by the matrix decoupling technique. Finally, the feasibility and effectiveness of the proposed method can be demonstrated through a numerical simulation. [ABSTRACT FROM AUTHOR]

This article solves the dual objective control problem for an energy storage system by distributed aperiodic sampled‐data controller under both connected static network and jointly‐connected switching network. The proposed sample‐and‐hold controller is composed of the leaderless consensus algorithm, which aims at state‐of‐energy balancing, and the leader‐following distributed observer, which aims at reference power tracking. By resorting to the generalized Krasovskii‐LaSalle theorem, it is proven that the closed‐loop system is uniformly exponentially stable using the so‐called limiting solution. There are two distinguished features of the proposed control scheme. First, the designs of the leaderless consensus algorithm and the leader‐following distributed observer can be accomplished independently. Second, the effectiveness of the control law can be guaranteed by properly tuning the feedback gains even for some extreme cases of drastic changes on sensing and sampling. Comprehensive simulation results are shown to examine the performance of the proposed control implement. [ABSTRACT FROM AUTHOR]

The cluster synchronization problem in a class of two-layer nonlinear networks via sampling-based event-triggered control scheme is considered in this paper. First, through a sector-like condition and the linear matrix inequalities (LMIs), a cluster synchronization criterion of the first layer is strictly given. Then, taking the average state of the corresponding subset of nodes in the first layer as the target, an event-triggered control protocol based on the sampled information is designed to synchronize the second layer to the first layer. By applying the time-delay system method and the event-triggered control principle, the sufficient condition for cluster synchronization of the second layer is obtained. In this process, an advanced Lyapunov functional, generalized Jensen's inequality and convex combination method are exploited to reduce the conservation of the results. In addition, numerical example further verifies the feasibility and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents the idea of implementing various techniques related to sampled-data control for magnetic levitation systems. The control laws are designed to track time-varying signals and employ the feedback linearization technique based on the approximate discrete-time model. State feedback control is introduced with the gains adjusted via the pole placement method. A positional form proportional-integral-derivative (PID) control uses the trapezoidal summation for the integral term and the backward difference method for the derivative term. An input-output linearization feedback control is the most promising one, which incorporates the integrator in addition to the position error and velocity error. The integral action involves the manipulation of regulation error and reduces it with time to improve performance. Finally, controllers were tested in real time for practical demonstration along with a comparison for comprehensive analysis.

This paper studies the sampled-data control issue for a luxury cruise (LC) with dynamic positioning system (DPS). The design method and mathematical model of LC is given. By constructing an improved time-dependent Lyapunov-Krasovskii function (LKF) by adding new useful terms, the sampling pattern is fully captured and less conservatism of the results are obtained. Based on the constructed the LKF, the new stability criterion is obtained and the sampled-data controller for LC with DPS is designed. Finally, an example is exhibited to prove that the proposed approach is valid and applicable.

In the manuscript, we report the dynamics of the Takagi–Sugeno (T–S) fuzzy memristor-based hidden system via sampled-data control. For an open-loop formulation, the system dynamics are studied. We found extreme events, hidden attractors, and trivial period doubling scenarios and confirmed them through numerical, analytical, statistical and experimental analyses. Furthermore, to enable stability analysis and control combination, the (T–S) fuzzy algorithm is employed to control the dynamics of a nonlinear system. First, we designed the sampled data fuzzy controller (SDFC) for the proposed system. Second, the Lyapunov–Krasovskii functional (LKF) strategy, novel integral inequality mechanisms, and certain sufficient conditions are determined by deriving the linear matrix inequalities (LMIs), which ensure the asymptotic stability of the system. Moreover, the sampled data control gains are computed for the large sampling interval, and numerically obtained results confirm the theoretical results. Additionally, a simple real-time analog electronic circuit is constructed, and experimental data is obtained, and finally, numerically simulated results were verified through MATLAB. [ABSTRACT FROM AUTHOR]

This paper studies the bipartite containment control problem for a network of wave equations. A distributed sampled-data control protocol is proposed such that the follower states converge to a convex hull spanned by the leader trajectories and their opposites. A criterion for bipartite containment control is derived by applying the constructed Lyapunov-Krasovskii functional. The well-posedness of the closed-loop system is verified using the semigroup and induction methods. Furthermore, the effectiveness of the theoretical results is demonstrated through numerical examples. [ABSTRACT FROM AUTHOR]

The issue of H ∞ stabilization for delayed memristive neural networks with dynamic quantization is considered. The aim is to design a quantized sampled-data controller guaranteeing that the closed-loop system is globally asymptotically stable with a prescribed H ∞ disturbance attenuation level. By means of set-valued maps and the differential inclusion theory, the network under consideration is transformed into a dynamic model subject to time-dependent bounded uncertainty. Then, two different time-dependent two-sided loop functionals are constructed for the non-necessarily and necessarily differential time delay situations, respectively. Two sufficient conditions on the stability and H ∞ performance are derived via using these constructed functionals and a few inequality techniques. On the foundation of these conditions, co-designs of the needed feedback gain and dynamic quantization parameter are presented. Finally, three examples are provided to verify the applicability of the quantized sampled-data controller design methods. [ABSTRACT FROM AUTHOR]

This article deals with the stability analysis of Lure type systems through aperiodic sampled‐data control laws, where the nonlinearity is assumed to be both sector and slope restricted. The proposed method is based on the use of a new class of looped‐functionals, which depends on the nonlinearity and its slope, and on a generalized Lure type function, that is quadratic on both the states and the nonlinearity and has a Lure‐Postnikov integral term. On this basis, conditions in the form of linear matrix inequalities to certify global or regional asymptotic stability of the closed‐loop system are obtained. These conditions are then used in optimization problems for computing the maximum intersampling interval or the maximum sector bounds for which the stability of the sampled‐data closed‐loop system is guaranteed. Numerical examples to illustrate the results are provided. [ABSTRACT FROM AUTHOR]

This article considers a finite‐time sampled‐data output feedback stabilization problem for a class of feed‐forward non‐holonomic systems. Due to that the states of the focused system are unavailable, a reduced‐order observer is designed to estimate the unavailable states with the sampled output detection of the non‐holonomic system, and two finite‐time sampled‐data stabilizers are respectively constructed by combining the backstepping and adding a power integral techniques. Based on the stepped and switching control strategies, the corresponding closed‐loop system can be globally finite‐time stable by the constructed stabilizers with allowable design constants and sampling period, and this can be proved by choosing an appropriate Lyapunov function candidate. In the end, two simulation examples, which include a numerical and a practical examples, are provided to verify the effectiveness of the proposed finite‐time stabilization method. [ABSTRACT FROM AUTHOR]

The stabilisation of the sampled-data control systems is addressed to tolerate large sampling intervals that tend to destroy the system's stability. A new sampling sequence is constructed from two sequences with two different sampling time intervals, where the short one corresponds to a stable system, while the long one produces an unstable system. The decay rate of the state trajectory of the former is utilised to overcome the growth rate of the latter, such that the resulting system with mixed sampling intervals is globally exponentially stable. A sufficient condition for stabilisation is given. It is applicable to the system with a single sampling interval under the missing data scenario. The examples are given to illustrate the proposed results. [ABSTRACT FROM AUTHOR]

The optimal guaranteed cost sampled‐data control problem is studied for high‐speed train systems. To keep the relative speed and relative displacement of each high‐speed train stable, and ensure the performance level of high‐speed trains at the same time, sufficient conditions are obtained for the optimal guaranteed cost sampled‐data controller of train systems. By the convex optimization method, the minimum upper bound of high‐speed train systems is obtained. At last, two examples are presented to substantiate the correctness of theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the consensus problem is investigated via sampled-data output feedback control for nonlinear multi-agent systems subject to output delay and false data injection attacks, where only the delayed sampling outputs are available. Firstly, employing the available information, an improved compensator that does not continuously communicate with neighbors is constructed to provide control signals. Secondly, in the absence and presence of false data injection attacks, two compensator-based sampled-data control protocols with different time-varying gains are put forward, respectively. Then, in combination with the Lyapunov–Krasovskii functional approach, the proposed protocols are capable of achieving consensus and compensating for the effect of output delay and attack signals. Furthermore, above results are extended to the multi-agent systems with more general nonlinearities. Particularly, the sampling period and the output delay are arbitrary positive constants, removing the constraints of upper bound. Finally, the feasibility of the proposed protocols is demonstrated by two practical simulations. [ABSTRACT FROM AUTHOR]

This paper studies the sampled-data second-order consensus of multiple Euler-Lagrange systems without knowing neighbors’ velocities. The second-order feedback control method of the Euler-Lagrange system naturally excludes the infinities in the control input that arise alongside sampled-data communication. It also avoids the auxiliary variable, which is seen as a disturbance. However, the control input is still discontinuous at sampling instants and not attainable for most real-world applications. The main goal of this work is to design a relatively smooth control input for the second-order consensus of Euler-Lagrange systems. The pulse-modulated scheme is employed to smoothen the discontinuities caused by the sampled communication. Necessary and sufficient consensus conditions are established through discretization and stability theory. It is shown that the shape of the pulse function also influences consensus in addition to its integral. Finally, numerical examples show that the proposed pulse-modulated control scheme smoothens the control inputs and ensures consensus under the criterion.

This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.

This paper proposes a sampled-data fuzzy controller design technique for an autonomous underwater vehicle (AUV) depth system represented by an interval type-2 (IT-2) fuzzy model, considering input saturation. In the Takagi–Sugeno (T–S) fuzzy model of an AUV depth system, surge velocity is chosen as a premise variable. To address the associated uncertainty with this variable, we employ the IT-2 fuzzy modeling technique. Also, the controller proposed in this paper utilizes time-varying gains, ensuring superior exponential stability compared with traditional fixed gain approaches. Furthermore, a membership function-dependent (MFD) H∞ criterion is employed to enhance robustness for each subsystem individually. Taking into account the mentioned aspects, the controller design condition is derived in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is validated through simulation examples.

This article discusses the stability problem of sampled-data-based dynamic positioning ships (DPSs) using Takagi–Sugeno (T-S) fuzzy models. Firstly, dynamic equations for sampled-data DPSs are established. Simultaneously combining several symmetric matrices with new integral terms, a novel Lyapunov–Krasovskii function (LKF) is constructed, which allows the information of a sampling pattern to be fully captured. Next, via the constructed LKF, the positive definiteness requirements of a LKF are further relaxed, and the conservatism of the result can be reduced. Consequently, stability criteria are given, and fuzzy sampled-data controllers are designed in terms of linear matrix inequality (LMI). Finally, a simulation example is provided to verify the superiority and applicability of the developed methods.

This paper discusses the sampled-data input-to-state stabilization for delayed semi-Markovian jump neural networks subject to external disturbance. First, a hybrid closed-loop system is formulated, which contains continuous-time state signal, disturbance input signal, discrete-time control signal, and jumping parameters of the semi-Markovian process. Then, two time-dependent and mode-dependent Lyapunov functionals are constructed corresponding to different assumptions about the activation functions. Subsequently, two sufficient conditions concerning the sampled-data controller design are derived to ensure the mean-square input-to-state stability for the hybrid closed-loop system by utilizing the proposed Lyapunov functionals, a few inequalities, as well as some stochastic analysis techniques. It is worth remarking that the present conditions are capable of ensuring mean-square exponential stability of the closed-loop system in the absence of external disturbances. Lastly, a numerical example is employed to verify the validity of the proposed input-to-state stabilization methods. [ABSTRACT FROM AUTHOR]