Sampled-data Control Research Articles

Stability and Stabilization of T–S Fuzzy Systems Under Sampled-Data Control via a Matrix-Separation-Based Inequality

Stability and Stabilization of T–S Fuzzy Systems Under Sampled-Data Control via a Matrix-Separation-Based Inequality

Enhanced Results on Sampled-Data Synchronization for Chaotic Neural Networks With Actuator Saturation Using Parameterized Control.

This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.

The Stabilization of a Nonlinear Permanent-Magnet- Synchronous-Generator-Based Wind Energy Conversion System via Coupling-Memory-Sampled Data Control with a Membership-Function-Dependent H∞ Approach

This study presents the coupling-memory-sampled data control (CMSDC) design for the Takagi–Sugeno (T-S) fuzzy system that solves the stabilization issue of a surface-mounted permanent-magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). A fuzzy CMSDC scheme that includes the sampled data control (SDC) and memory-sampled data control (MSDC) is designed by employing a Bernoulli distribution order. Meanwhile, the membership-function-dependent (MFD) H∞ performance index is presented, mitigating the continuous-time fuzzy system’s disturbances. Then, by using the Lyapunov–Krasovskii functional with the MFD H∞ performance index, the data of the sampling pattern, and a constant signal transmission delay, sufficient conditions are derived. These sufficient conditions are linear matrix inequalities (LMIs), ensuring the global asymptotic stability of a PMSG-based WECS under the designed control technique. The proposed method is demonstrated by a numerical simulation implemented on the PMSG-based WECS. Finally, Rossler’s system demonstrates the effectiveness and superiority of the proposed method.

Switched sampled-data-based membership function-dependent [formula omitted] control for PMSG-based WTS with actuator failures

This paper deals with the problem of sampled-data-based H∞ control design for nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine system (WTS) subject to actuator failures. First, the Takagi-Sugeno (T-S) fuzzy system is employed to handle the nonlinearities of presented model. Next, by employing a Bernoulli stochastic variable, the general scenario of switched coupling memory sampled-data control (CMSDC) is designed which contains the sampled-data control (SDC) and memory SDC scheme as special cases. By combining the membership function-dependent (MFD) asymmetric looped-type Lyapunov-Krasovskii functional (LKF) and employing the available information of membership functions in H∞ performance, the delay-dependent stabilization conditions of considered PMSG-based WTS are derived through the switched CMSDC technique. Meanwhile, the disturbances of considered systems are attenuated by taking the advantage of the newly proposed MFD H∞ performance. Eventually, the simulation results are given to demonstrate the efficacy and applicability of the proposed CMSDC technique.

Finite-time H∞ synchronization of semi-Markov jump neural networks with two delay components with stochastic sampled-data control

This article investigates the finite-time H∞ synchronization for semi-Markov jump neural networks with two delay components based on stochastic sampled data control. Additionally, the parametric uncertainties are randomly varying which follows the Bernoulli distributed sequences. In the stochastic sampled data control, the sampling interval ′m′ is supposed to be two different values in the time-varying component with given probability conditions. By constructing triple and quadruple integral term in the Lyapunov-Krasovskii functional (LKF) a new integral inequality technique is addressed to derive the main results. Dissimilar from previous literature, involving the new integral inequality, a delay dependent finite-time H∞ synchronization requirements are acquired with regard to linear matrix inequalities (LMIs). In the end, the effectiveness of the considered stochastic sampled data control finite time synchronization scheme is highlighted by numerical examples.

Design of fuzzy dissipative sampled-data control for nonlinear wind turbine systems with random packet losses and communication delays

Design of fuzzy dissipative sampled-data control for nonlinear wind turbine systems with random packet losses and communication delays

Stabilization of Takagi–Sugeno fuzzy Hidden Markov Jump Systems with memory sampled-data control

The stability of Takagi–Sugeno fuzzy hidden Markovian jump systems by employing a memory sampled-data control (SDC) scheme that includes a constant signal transmission delay is investigated. The asynchronous situation between the system plant and the controller is represented using the hidden Markov model. The stability of the provided model is ensured by implication of the fuzzy time-scheduled Lyapunov functional (LF), and the sufficient conditions are derived in the form of linear matrix inequalities (LMIs). This simplifies the utilization and generalization of the analysis results, as they exhibit convexity in the subsystem matrix. Additionally, it diminishes the influence of external disturbances by employing the H∞ norm bound. To acquire the required time-dependent memory SDC, it is necessary to solve this set of LMIs. The efficacy and feasibility of the proposed method is established by attaining stability for a chaotic Lorenz system using the T–S fuzzy Hidden Markov Jump Systems with Memory SDC.

Mean-square exponential stabilization of memristive neural networks: Dealing with replay attacks and communication interruptions

This paper investigates the mean-square exponential stabilization (MSES) of memristive neural networks (MNNs) under replay attacks and communication interruptions. The research will revolve around the following two questions: Firstly, facing replay attacks and communication interruptions, how to design an appropriate controller? Secondly, how to ensure the MSES of MNNs under higher replay attack rate and communication interruption rate? To address these challenges, a novel intermittent sampled-data control scheme is established by considering the mathematical characteristics of replay attacks and communication interruptions. Furthermore, interval-dependent Lyapunov functions are constructed based on the Lyapunov stability theory and inequalities techniques, and two sufficient criteria for MSES of MNNs under the mentioned risks are derived. Thus, the control gain is obtained by solving a series of linear matrix inequalities. In addition, two algorithms are designed to investigate the maximum allowable replay attack rate and communication interruption rate for the system, respectively. Finally, two numerical examples are given to verify the effectiveness of the proposed scheme.

Time-Varying Formation Tracking Control of Multi-Leader Multiagent Systems With Sampled-Data

In this paper, the time-varying formation tracking (TVFT) problem of multi-leader multi-agent systems (MASs) under sampled-data control is studied. First, an improved TVFT criterion is derived by employing a general looped-functional and a zero integral functional. Then a new TVFT controller is designed based on our proposed method. Unlike the existing works, the TVFT controller can allow for bigger sampling intervals of multi-leader MASs. The results demonstrate that the states of the followers can form the designated formation and rotate around the convex combination formed by the multiple leaders. In addition, we generalize the results of TVFT to the sampled-data consensus for MASs, a sampled-data consensus controller which can reduce communication consumption is designed. Meanwhile, the values of the allowable maximum sampling intervals (AMSIs) are calculated. Finally, the superiority and availability of the theoretical results are verified by two simulation experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to address the problem of TVFT for MAS with multiple leaders. It has been applied to several practical engineering systems, e.g., satellite formation control and unmanned air vehicles (UAVs) formation control. Note that information exchange is a prerequisite to ensure safe and stable control of multi-UAV formation. However, continuous-time communication is difficult to be realized in the actual environment, and it will inevitably consume a lot of energy. In addition, sampled-data controller for MAS with a small sampling interval may cause network congestion. Therefore, a sampled-data controller which allows for a bigger sampling interval for MAS is designed to save network resources in this paper. Through simulation experiments, it can be proved that the followers not only track the leaders, but also form the designated formation.

A Closed-Loop Using Sampled-Data Controller for a New Nonisolated High-Gain DC–DC Converter

A Closed-Loop Using Sampled-Data Controller for a New Nonisolated High-Gain DC–DC Converter

Exponential State Estimation for Delayed Competitive Neural Network Via Stochastic Sampled-Data Control with Markov Jump Parameters Under Actuator Failure

Abstract This article examines the problem of estimating the states of Markovian jumping competitive neural networks, where the estimation is done using stochastic sampled-data control with time-varying delay. Instead of continuously measuring the states, the network relies on sampled measurements, and a sampled-data estimator is proposed. The estimator uses probabilistic sampling during two sampling periods, following a Bernoulli distribution. The article also takes into account the possibility of actuator failure in real systems. To ensure the exponentially mean-square stability of the delayed neural networks, the article constructs a Lyapunov-Krasovskii functional (LKF) that includes information about the bounds of the delay. The sufficient conditions for stability are derived in the form of linear matrix inequalities (LMIs) by employing modified free matrix-based integral inequalities. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.

Sampled-data event-triggered control with sampling-independent positive guarantees on inter-event times

To fit with a digital environment in networked control systems, sampled-data event-triggered control (ETC) has been proposed where the event-triggering mechanisms sense and process information only at discrete sampling instants (not necessarily periodically). One deficiency in the previous study on sampled-data ETC is the lack of analysis on transmission performance, which yields the potential occurrence of an undesirable phenomenon that the minimum inter-event time is always equal to the minimum inter-sampling interval, no matter how small the latter is. To overcome this drawback, in this paper, we propose a novel sampled-data ETC scheme such that the lower bounds of inter-event times have sampling-independent positive guarantees. A linear networked control system is considered under observer-based controllers, external disturbances, and multiple communication channels. The improved transmission performance is due to the utilization of dynamic event-triggering conditions and some mean-rate error signals, which are the ratios between network-induced errors and some time-increasing functions. After modeling the closed-loop dynamics into a hybrid system, sufficient conditions on the upper bound of inter-sampling intervals and parameters in ETC are provided to ensure input-to-state stability (rather than its practical version) and sampling-independent positive guarantees simultaneously. Furthermore, a new tradeoff relationship between the sampling and transmission performance is revealed: a faster sampling frequency is conducive to improving inter-event times. Finally, a linearized model of an inverted pendulum is simulated to illustrate the efficiency and feasibility of the obtained results.

Pinning event-triggered sampled-data synchronization of coupled reaction–diffusion neural networks

In this paper, the synchronization problem of coupled reaction–diffusion neural networks (CRDNNs) is considered by using pinning event-triggered sampled-data (PETSD) control method under spatially point measurements. To save the limited transmission channel, a PETSD control mechanism by controlling a small fraction of the nodes is proposed to decrease the update frequency of controller and the unnecessary SD. Then, based on the Lyapunov–Krasovskii functional (LKF) and inequality techniques, sufficient conditions for exponential stability of the synchronization error system presented by linear matrix inequalities (LMIs) can be derived through the designed PETSD controller. Finally, simulation results of one numerical example are presented to demonstrate the effectiveness of the proposed design approach.

A switched system approach to exponential stabilization of interval type-2 fuzzy nonlinear systems with packet dropouts

This paper investigates the exponential stabilization problem for interval type-2 (IT2) fuzzy system with parameter uncertainties and packet dropouts. The sample-and-hold characteristic is simulated by employing an input delay methodology, and we present a model for the packet dropout phenomenon using switched approach. Some novel two-side delay-dependent looped-functionals are proposed by considering state information in the intervals from tr to t and t to tr+1 which relaxes the monotonic constraint of the Lyapunov–Krasovskii functional (LKF) and enhances the utilization of the actual sampling pattern. Additionally, some free-weighting matrices, slack matrices, the bound information in membership functions (MFs) and the signs of the first-order time derivative of fuzzy MFs are used to obtain a novel relaxed sufficient conditions for exponential stabilization of interval type-2 fuzzy systems (IT2 FS). Based on the LKF theory, new relaxed sufficient conditions with linear matrix inequalities (LMIs) constraints are derived. According to the criterion, sampled-data controller is devised to ensure that the closed-loop system is exponential stabilization. Finally, an illustrative example is presented to demonstrate the practicality and effectiveness of the method proposed. Comparative analysis highlights the simplicity and practicality of the suggested algorithm.

Decentralized Sampled-Data Control for Stochastic Disturbance in Interconnected Power Systems With PMSG-Based Wind Turbines.

The presented work is concerned with the stability performance of frequency response for interconnected power systems (IPSs) with permanent magnet synchronous generator (PMSG)-based wind turbines (WTs) via a decentralized control scheme against external and stochastic disturbances. To do this, initially, the state-space model is derived when a PMSG is penetration into IPSs. Differing from existing works, IPSs with PMSG-based WTs, including the stochastic disturbance, are modeled as a stochastic state-space model. Then, decentralized sampled-data load frequency control is designed to regulate the frequency response of the proposed model against the deterministic and stochastic noises. By constructing bilateral looped Lyapunov functional and using Itôs formula, stochastic sufficient conditions are derived, which ensure that the closed-loop form of the proposed model is asymptotically stable in the mean square with H∞ performance index γ . Finally, three-area IPSs with PMSG-based WTs are validated with derived sufficient conditions. The simulation results confirmed the better-stability performance of frequency response for the proposed stochastic IPSs with PMSG-based WTs.

Fuzzy Boundary Sampled-Data Control for Nonlinear Parabolic DPSs.

For a nonlinear parabolic distributed parameter system (DPS), a fuzzy boundary sampled-data (SD) control method is introduced in this article, where distributed SD measurement and boundary SD measurement are respected. Initially, this nonlinear parabolic DPS is represented precisely by a Takagi-Sugeno (T-S) fuzzy parabolic partial differential equation (PDE) model. Subsequently, under distributed SD measurement and boundary SD measurement, a fuzzy boundary SD control design is obtained via linear matrix inequalities (LMIs) on the basis of the T-S fuzzy parabolic PDE model to guarantee exponential stability for closed-loop parabolic DPS by using inequality techniques and a acrlong LF. Furthermore, respecting the property of membership functions, we present some LMI-based fuzzy boundary SD control design conditions. Finally, the effectiveness of the designed fuzzy boundary SD controller is demonstrated via two simulation examples.

Stabilization analysis for nonlinear interconnected system with memory based coupled sampled data control via quantum-inspired genetic algorithm

In the realm of modern interconnected systems, achieving stability represents a pivotal challenge due to the complex dynamics among various components. This paper presents a new approach to stability analysis, leveraging linear matrix inequalities and Lyapunov-Krasovskii functionals. The framework of memory-based coupling sampled data control is enhanced by a quantum genetic algorithm. By incorporating linear matrix inequality approach, this study establishes a robust mathematical foundation for characterizing stability conditions and synthesizing control gains. This innovative integration provides a powerful toolset to optimize control parameters while mitigating the impact of disturbances. The simulation findings are explicitly based on experimental values of two-area interconnected power systems with doubly fed induction generator based wind farm, which guarantees the asymptotic stability of the proposed controller.

Nonfragile Exponential Synchronization for Delayed Fuzzy Memristive Inertial Neural Networks via Memory Sampled-Data Control

Nonfragile Exponential Synchronization for Delayed Fuzzy Memristive Inertial Neural Networks via Memory Sampled-Data Control

[formula omitted] sampled-data load frequency control with time-varying delays considering delay compensation

Time delays in load frequency control (LFC) systems may deteriorate the control performance or cause instability. How to mitigate such adverse effects of delays in a simple but effective way is worth investigating and motivates this work. For sampled-data LFC systems with time-varying delays, this paper introduces two delay compensation techniques, namely linear and division-based methods, aimed at enhancing the robustness of LFC. A stability criterion based on H∞ is derived to ensure LFC system stability under delay compensation. Building upon this criterion, we propose an H∞ controller optimization method to tune delay compensation controller gains offline by searching for a minimum H∞ disturbance attenuation level and effectively segmenting the geometric region formed by the delays. The effectiveness of the proposed delay compensation techniques and the controller design method is verified via numerical examples on an LFC system with both generation unit and energy storage participation.

Submission to Special Issue to Explainable Representation Learning-Based Intelligent Inspection and Maintenance of Complex Systems: Synchronization of Inertial Neural Networks With Unbounded Delays via Sampled-Data Control.

This article addresses the synchronization issue for inertial neural networks (INNs) with heterogeneous time-varying delays and unbounded distributed delays, in which the state quantization is considered. First, by fully considering the delay and sampling time point information, a modified looped-functional is proposed for the synchronization error system. Compared with the existing Lyapunov-Krasovskii functional (LKF), the proposed functional contains the sawtooth structure term V8(t) and the time-varying terms ex(t-βħ (t)) and ey(t-βħ (t)) . Then, the obtained constraints may be further relaxed. Based on the functional and integral inequality, less conservative synchronization criteria are derived as the basis of controller design. In addition, the required quantized sampled-data controller is proposed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to show the effectiveness and superiority of the proposed scheme in this article.