Sampled-data Control Scheme Research Articles

A sampled-data control scheme for fractional-order fuzzy systems via looped-functional

A sampled-data control scheme for fractional-order fuzzy systems via looped-functional

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.

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.

Stability analysis of interval type-2 sampled-data polynomial fuzzy-model-based control system with a switching control scheme

The stability of an interval type-2 (IT2) sampled-data (SD) polynomial fuzzy-model-based control system with a switching control scheme is studied in this paper. The uncertain nonlinear plant is depicted via an IT2 polynomial fuzzy model. To realize control, a switching IT2SD polynomial fuzzy controller is generated. This paper adopts a switching control scheme with a variable sampling period. The modeling domain consists of several sub-domains, and each sub-domain corresponds to a local IT2SD polynomial fuzzy controller. These local IT2SD polynomial fuzzy controllers form the switching IT2SD polynomial fuzzy controller. To aid in the stability analysis, this paper adopts a looped-functional-based technique. The imperfect premise matching concept is brought in to solve the mismatch dilemma caused by the SD control strategy and uncertainties. For decreasing the conservativeness, this paper takes into account the state information as well as the information of IT2 membership functions. The stability analysis is performed for each sub-domain, providing the potential for further relaxation. As polynomials exist in the stability conditions, this paper employs the sum-of-squares method for the stability investigation. The simulation outcomes confirm the efficacy of the proposed method.

Reachable set performance and quantized sampled-data synchronization analysis of neural networks under random packet dropouts via enhanced looped functional

The study aims to present the synchronization concerns of neural networks with stochastic packet dropouts utilizing a quantized memory sampled-data control (QMSDC) scheme and the problem of reachable set analysis (RSA) for NNs under the influence of external disturbances are discussed. To do this, a novel free-weighting matrix integral inequality (FWMII) is proposed with an augmented Lyapunov-functional. Furthermore, at this time, we show that the proposed inequality is less conservative due to the delay information. In the meantime, a novel enhanced looped function is created that utilizes not only sampling information but also incorporates both transmission delay information and a quantized sampling pattern. Next, using the proposed FWMII technique, several sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to suggest how an augmented closed-loop system can achieves stochastic mean-square exponential synchronization under random packet loss situations. Finally, the proposed approach demonstrates its usefulness and superiority by comparing it with existing methods through numerical simulation of the secure communications.

Fuzzy membership-function-dependent design of aperiodic sample-data control scheme for nonlinear PMSG-based WECS with quantization measurements via refined looped Lyapunov functional

This manuscript investigates the fuzzy memory sampled-data control (MSDC) scheme for the nonlinear permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) with quantization measurements. First, due to the complex nonlinearity, a Takagi-Sugeno (T-S) fuzzy modeling approach is successfully applied to the dynamics of nonlinear PMSG-based WECS. Second, by using the information of both quantized aperiodic sampling pattern and signal transmission delay, an H∞-based fuzzy quantized controller is put forward to ensure the stable performance as well as disturbance suppression index of the considered PMSG model by utilizing the refined looped Lyapunov functional (RLLF) approach. This RLLF perfectly utilizes more realistic information about the time derivative of the fuzzy membership function and actual sampling pattern. Then, two new integral inequalities are introduced to approximate the quadratic integral terms arising from the derivative of RLLF. Next, we establish some less conservative stabilization results in terms of linear matrix inequalities (LMIs) using the constructed RLLF, along with the proposed integral inequalities and fuzzy membership function-dependent (MFD) switching ideas. Finally, the simulation studies of PMGS-based WECS are validated numerically, and then a comparative example is provided to emphasize the superiority and relevance of the suggested approach.

Hypertracking and Hyperrejection: Control of Signals Beyond the Nyquist Frequency

This paper studies the problem of signal tracking and disturbance rejection for sampled-data control systems, where the pertinent signals can reside beyond the so-called Nyquist frequency. In light of the sampling theorem, it is generally understood that manipulating signals beyond the Nyquist frequency is either impossible or at least very difficult. On the other hand, such control objectives often arise in practice, and control of such signals is much desired. This paper examines the basic underlying assumptions in the sampling theorem and pertinent sampled-data control schemes, and shows that the limitation above can be removed by assuming a suitable analog signal generator model. Detailed analysis of multirate closed-loop systems, zeros and poles are given, which gives rise to tracking or rejection conditions. Robustness of the new scheme is fully characterized; it is shown that there is a close relationship between tracking/rejection frequencies and the delay length introduced for allowing better performance. Examples are discussed to illustrate the effectiveness of the proposed method here.

Spatiotemporal sampled-data fuzzy control for switched singularly perturbed PDE systems with average dwell-time switching mechanism

This paper investigates a spatiotemporal sampled-data fuzzy control strategy for switched singularly perturbed partial differential equation (PDE) systems, where the systems’ operation modes obey average dwell-time switching mechanism. To efficiently deal with nonlinear terms and guarantee the system stability for the considered systems, a spatiotemporal sampled-data fuzzy control scheme is developed. Furthermore, based on the fact that mode mismatch phenomena during switching and sampling, through formulating novel Lyapunov functionals (LFs) with the discontinuous terms and mode-dependent two-sided looped-functionals, which can fully utilize the state information of the sampling period, a new exponential stability criterion is provided for the target systems. Finally, an example is provided to prove the validity of the proposed control approach.

Adaptive event-triggered extended dissipative synchronization of delayed reaction–diffusion neural networks under deception attacks

Under spatially averaged measurements (SAMs) and deception attacks, this article mainly studies the problem of extended dissipativity output synchronization of delayed reaction–diffusion neural networks via an adaptive event-triggered sampled-data (AETSD) control strategy. Compared with the existing ETSD control methods with constant thresholds, our scheme can be adaptively adjusted according to the current sampling and latest transmitted signals and is realized based on limited sensors and actuators. Firstly, an AETSD control scheme is proposed to save the limited transmission channel. Secondly, some synchronization criteria under SAMs and deception attacks are established by utilizing Lyapunov–Krasovskii functional and inequality techniques. Then, by solving linear matrix inequalities (LMIs), we obtain the desired AETSD controller, which can satisfy the specified level of extended dissipativity behaviors. Lastly, one numerical example is given to demonstrate the validity of the proposed method.

Nonfragile Memory-Based PD-Like Sampled-Data Consensus Control for Nonlinear Multiagent Systems With Time-Varying Communication Delays

In this article, the consensus problem of nonlinear multiagent systems (MASs) with time-varying communication delays (TVCDs) is addressed by proposing a novel nonfragile memory-based sampled-data control approach. Different from the existing sampled-data control schemes, an improved memory-based proportional derivative like a sampled-data control scheme based on the information of neighbors is proposed. Besides, considering the inaccuracy and perturbation of controller, a nonfragile control method is also adopted to design the controller. Then, instead of the general quadratic type of the Lyapunov–Krasovskii functional, a delay-product-type quadratic is presented by taking full advantage of the information of TVCDs, and the positive definite constraint in the entire time domain is weakened to the sampled instants on account of adopting the two-sided looped-functional approach. Furthermore, less-conservative consensus criteria for MASs are developed. By solving linear matrix inequalities, the desired control gain matrices and larger sampled-data periods are achieved. Finally, illustrative examples are employed to demonstrate the effectiveness of the designed control strategy.

Resilient Sampled-Data Control for Stabilization of T--S Fuzzy Systems via Interval-Dependent Function Method: Handling DoS Attacks

This paper is concerned with the resilient sampled-data control for stabilization of T-S fuzzy systems in the presence of denial-of-service (DoS) attacks. To describe the effects of DoS attacks, an appropriate mathematical model is established to characterize the complicated dynamical behaviors of DoS attacks and periodic sampling mechanism. On this basis, a resilient sampled-data control scheme including communication protocols and security controller gains is proposed to account for the effects of DoS attacks. Meanwhile, a novel index is developed to measure the anti-attack ability and security level. By utilizing the information of DoS attacks and resilient sampling intervals, a continuous interval-dependent Lyapunov function is constructed which does not increase at the resilient sampling instants. Then, according to the designed interval-dependent function, several inequalities estimation techniques, convex combination approach, and together with discrete-time Lyapunov theory, two sufficient criteria are derived to guarantee that the closed-loop T-S fuzzy systems are globally asymptotically stable with prespecified security levels subject to DoS attacks. The advantages of designed function are well analyzed in contrast with the previous discontinuous Lyapunov functionals. At last, two simulation examples are given to demonstrate the validity and effectiveness of the obtained results.

Intermittent sampled-data control for exponential synchronization of chaotic delayed neural networks via an interval-dependent functional

This paper focuses on the exponential synchronization of chaotic delayed neural networks (CDNNs) via the intermittent sampled-data control (ISC). To save the control cost and limited bandwidth, an ISC strategy which integrates the merits of the intermittent control (IC) scheme and the sampled-data control (SC) scheme is proposed. With the proposed ISC scheme, an interval-dependent Lyapunov functional (IDLF) is established for the synchronization control systems. The main characteristic of this functional is that it takes full advantage of the available state information of both working intervals and sleeping intervals. Due to the continuity of the proposed functional, some restrictive conditions on Lyapunov matrices are removed. In addition, some less conservative synchronization criteria are established by means of a combination of the Lyapunov stability theory, the reciprocally convex inequality and the Wirtinger inequality. Meanwhile, an algorithm for the ISC gain is effectively designed to realize the synchronization of master and slave CDNNs. Furthermore, a qualitative relationship between the duty ratio (DR) and the system decaying rate is analyzed. In the end, two examples are given to reveal that the proposed results are feasible and reasonable.

Induced $\mathcal {L}_\infty$ Quantized Sampled-Data Control for T–S Fuzzy System With Bandwidth Constraint

This article proposes an induced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> sampled-data control problem for the Takagi–Sugeno (T–S) fuzzy system with dynamic quantization, in which the communication channel is assumed to be bandwidth-constrained. First, according to the input delay approach, the dynamics of the considered closed-loop system is modeled by a delay system with constrained nonlinear term. Then, for the bandwidth-constrained communication channel, a constraint condition is introduced to ensure that the adjustable parameter of the dynamic quantization strategy is limited by a bounding region. The main purpose of the problem addressed is to design a controller, such that the closed-loop system guarantees the asymptotic stability with an induced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> performance index. The sufficient conditions of stability with induced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> control performance are given as the basis of controller design. Furthermore, the induced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_\infty$</tex-math></inline-formula> sampled-data control scheme and the improved updating rule of dynamic parameter are proposed. Finally, an example shows the effectiveness of the the proposed controller design scheme.

Non-fragile sampled-data control for synchronization of chaotic fractional-order delayed neural networks via LMI approach

This study addresses the master and slave synchronization problem of chaotic fractional-order delayed neural networks using a non-fragile sampled-data control (NFSDC) scheme which includes uncertainty information in the control gain matrix. An appropriate Lyapunov functional is constructed with information about sampling instants. A new, improved fractional-order inequality is developed to estimate the integral term. Then, based on the new integral inequality, the delay-dependent stability criteria are derived in the form of linear matrix inequality, which guarantees the asymptotic stability of the fractional-order error systems. This implies that the proposed NFSDC can synchronize the fractional-order master and slave systems. Moreover, numerical simulation results illustrate the synchronization nature and the influence of fractional derivatives in the system dynamics. Finally, it can be concluded that the proposed method and control scheme are effective.

Sampled-data control through model-free reinforcement learning with effective experience replay

Reinforcement Learning (RL) based control algorithms can learn the control strategies for nonlinear and uncertain environment during interacting with it. Guided by the rewards generated by environment, a RL agent can learn the control strategy directly in a model-free way instead of investigating the dynamic model of the environment. In the paper, we propose the sampled-data RL control strategy to reduce the computational demand. In the sampled-data control strategy, the whole control system is of a hybrid structure, in which the plant is of continuous structure while the controller (RL agent) adopts a discrete structure. Given that the continuous states of the plant will be the input of the agent, the state–action value function is approximated by the fully connected feed-forward neural networks (FCFFNN). Instead of learning the controller at every step during the interaction with the environment, the learning and acting stages are decoupled to learn the control strategy more effectively through experience replay. In the acting stage, the most effective experience obtained during the interaction with the environment will be stored and during the learning stage, the stored experience will be replayed to customized times, which helps enhance the experience replay process.The effectiveness of proposed approach will be verified by simulation examples.

New Stabilization Conditions for Fuzzy-Based Sampled-Data Control Systems Using a Fuzzy Lyapunov Functional

This paper focuses on the stabilization problem of Takagi-Sugeno fuzzy systems via a sampled-data controller using a fuzzy dependent functional. By employing a property of convex combination, a new approach to deal with the time derivatives of the fuzzy membership functions (FMFs) in the stabilization conditions is proposed, and less conservative conditions are derived in the form of linear matrix inequalities (LMIs). Moreover, the proposed approach introduces a switching function which opens up possibilities to use a switched controller and take advantage of its benefits well known in the literature. Therefore, two sampled-data control strategies are proposed, where the first one is a fuzzy controller and the second is a robust switched controller, that does not require the expressions of the FMFs to implement the control law, which guarantees the robustness of the controlled system in cases where the FMFs depend on uncertain parameters. Finally, the effectiveness of the proposed strategies is verified by two examples.

Resilient Control for Multiagent Systems With a Sampled-Data Model Against DoS Attacks

To reduce the computational burden and resist the denial-of-service (DoS) attacks, a resilient distributed sampled-data control scheme is proposed for multiagent systems. The agent states are sampled periodically by the sensors. DoS attacks disrupt the data communication from transmitters to controllers randomly or periodically with a limited duration time. Information on DoS attacks can be obtained by introducing novel logic processors embedded in corresponding controllers. Next, the problem of resilient control can be converted into one concerned with the upper and lower bound of the sampling interval of an aperiodic sampled-data control system. Some sufficient criteria for developing resilient distributed controllers are derived using the novel looped Lyapunov functional approach and the free-matrix-based inequality method. Finally, two illustrative examples, unmanned aerial vehicles and the two-mass-spring systems, are provided to demonstrate the efficiency of the proposed resilient distributed sampled-data control protocols against the DoS attacks.

Dissipative-based sampled-data control for T-S fuzzy wind turbine system via fragmented-delayed state looped functional approach

This study investigates thedissipative-based fuzzy sampled-data control scheme for variable-speed wind turbine system (WTS) by fragmented-delayed state looped functional framework. The main objective of this study is to stabilize the nonlinear variable-speed WTS and enhance its dynamic performance. To do this, initially, the proposed nonlinear variable-speed WTS is transformed into linear subsystems based on the Takagi–Sugeno (T-S) fuzzy approach. Then, the concept of coupling leakage time-varying delay is proposed to construct a more generalized T-S fuzzy model. After that, to minimize design conservatism, an improved fragmented- delayed state looped-Lyapunov functional is developed to fully utilize the advantages of the variable characteristics related to the actual sampling pattern. Besides, by applying the proposed new integral inequalities, some sufficient conditions are derived to ensure the addressed system is asymptotically stable under an optimizing performance index. Finally, numerical simulations are given to verify the effectiveness and feasibility of the proposed control scheme. The essential outcome of the proposed approach is that it can provide a superior dissipative performance index under the maximal sampling period.

Sampled-data Control of Probabilistic Boolean Control Networks: A Deep Reinforcement Learning Approach

The rise of reinforcement learning (RL) has guided a new paradigm: unraveling the intervention strategies to control systems with unknown dynamics. Model-free RL provides an exhaustive framework to devise therapeutic methods to alter the regulatory dynamics of gene regulatory networks (GRNs). This paper presents an RL-based technique to control GRNs modeled as probabilistic Boolean control networks (PBCNs). In particular, a double deep-Q network (DDQN) approach is proposed to address the sampled-data control (SDC) problem of PBCNs, and optimal state feedback controllers are obtained, rendering the PBCNs stabilized at a given equilibrium point. Our approach is based on options, i.e., the temporal abstractions of control actions in the Markov decision processes (MDPs) framework. First, we define options and hierarchical options and give their properties. Then, we introduce multi-time models to compute the optimal policies leveraging the options framework. Furthermore, we present a DDQN algorithm: i) to concurrently design the feedback controller and the sampling period; ii) wherein the controller intelligently decides the sampled period to update the control actions under the SDC scheme. The presented method is model-free and offers scalability, thereby providing an efficient way to control large-scale PBCNs. Finally, we compare our control policy with state-of-the-art control techniques and validate the presented results.