Non-fragile Sampled-data Control Research Articles

Nonlinear Multi-Agent Systems Consensus via Delayed Non-Fragile Sampled-Data Control

Nonlinear Multi-Agent Systems Consensus via Delayed Non-Fragile Sampled-Data Control

Non-fragile sampled-data control for synchronizing Markov jump Lur'e systems with time-variant delay

Non-fragile sampled-data control for synchronizing Markov jump Lur'e systems with time-variant delay

Dynamical analysis and reachable set estimation of T-S fuzzy system with permanent magnet synchronous motor

In this study, we will present the dynamical analysis for permanent magnet synchronous motors (PMSM). Our goal is to identify an ellipsoid that contains the state trajectory of the system in as small a form as possible in the presence of control. First, we designed the non-fragile sampled-data fuzzy controller (NFSDFC) for the PMSM model. Second, the Lyapunov-Krasovskii functional (LKF) strategy, novel integral inequality mechanisms, and certain sufficient conditions are determined, which ensure an ellipsoidal bound of reachable sets for the closed-loop of a system with input constraints derived in terms of linear matrix inequalities (LMIs). Meanwhile, under the larger sampling interval, the corresponding sampled-data controller gains are designed. Finally, numerical examples are given to validate the derived theoretical results.

Secure P2P Nonfragile Sampled-Data Controller Design for Nonlinear Networked System under Sensor Saturation and DoS Attack

Secure P2P Nonfragile Sampled-Data Controller Design for Nonlinear Networked System under Sensor Saturation and DoS Attack

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.

Stabilization of PMSG-based wind turbine systems with sampling information: Dynamic delay partition method

This paper addresses the novel stabilization criteria for a nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS) with sampled-data information, subject to the uncertainty and controller gain variations. In this regard, the nonlinearity in PMSG-based WTS is conferred by the Takagi–Sugeno (T–S) fuzzy systems. Next, by employing the full advantages of the variable characteristic related to the sampling pattern, a novel looped-type asymmetric augmented Lyapunov-Krasovskii functional (LKF) is developed. Then, the dynamic delay partition method is the first time to be applied for the non-fragile sampled-data control design of proposed T–S fuzzy systems. Also, the less conservativeness stabilization criteria for T–S fuzzy systems are obtained in the form of linear matrix inequality. In the end, the numerical examples confirm the applicability and efficiency of the presented results.

Stability of T-S fuzzy system under non-fragile sampled-data [formula omitted] control using augmented Lyapunov-Krasovskii functional

This article is concerned with the non-fragile sampled-data control for T-S fuzzy system with parameter uncertainties. Firstly, a novel augmented Lyapunov-Krasovskii functional with sufficient sampled-data information is constructed. And a novel h(t)-depended exponential stability criterion with H∞ performance is gotten by reciprocally convex matrix inequality. Beyond that, compared with the existing methods, the gain matrices for non-fragile sampled-data controller expected are less conservative by linear matrix inequality technique. And numerical examples are provided to support the viability and validity of the results.

Improved Nonfragile Sampled-Data Event-Triggered Control for the Exponential Synchronization of Delayed Complex Dynamical Networks

The exponential synchronization of complex dynamical networks (CDNs) under improved nonfragile sampled-data event-triggered control (INFSDETC) is investigated in this study. A meaningful yet challenging issue is solved, namely, it can adjust the triggering mode and the triggering frequency to adapt to more situations in the event-triggered scheme in which it is able to adjust the triggering condition exponentially and linearly, and dynamically adjust the triggering according to the time and state. By using control theory and Lyapunov analysis theory, an improved event-triggered controller was constructed for more intelligent control and to ensure exponential synchronization for CDNs. Lastly, significant numerical simulation examples are developed to show the usefulness and the performance of the proposed methods.

Hidden Markov model based non-fragile sampled-data control design for mode-dependent fuzzy systems with actuator faults

This paper investigates the dissipativity-based asynchronous sampled-data control design for a class of Takagi–Sugeno fuzzy Markovian jump systems along with time delay, gain fluctuations, and actuator failures. To do this, the hidden Markov model is introduced to describe the phenomenon of asynchronization between the controller and the given fuzzy jump systems. Then, a novel mode-dependent Lyapunov–Krasovskii functional is constructed to achieve the stability conditions and expand the maximum sampling period of fuzzy systems. The main purpose of this study is to design the asynchronous non-fragile reliable control with sampled-data information such that the mode-dependent fuzzy system is stochastically stable and possesses the dissipativity performance. For this, a sufficient condition is derived for the proposed system in the form of linear matrix inequalities. Meanwhile, the less conservative results and the desired controller are attained by solving the inequalities. At last, the validity and the superiority of our design technique are demonstrated by numerical examples.

Nonfragile Sampled-Data Control of T–S Fuzzy Systems With Time Delay

To reduce the influence of controller uncertainties and lower the computational burden of the system, a novel nonfragile sampled-data control scheme is proposed in this article for the Tagaki–Sugeno fuzzy system with time delay. First, an improved Lyapunov–Krasovskii functional is established with adjusted looped functional, which contains not only the current states, but the delayed states. Then, utilizing the Wirtinger inequality and improved single integral inequality, the new stability criteria are proposed with less conservativeness. By calculating the solution of linear matrix inequalities, the nonfragile controller parameters can be acquired. Furthermore, the sampled-data fuzzy controller leads to a good stable performance for the system, even under the possible gain fluctuations. Finally, three examples are given to verify the effectiveness and superiority of the proposed control scheme.

State quantized sampled-data control design for complex-valued memristive neural networks

In this paper, several resultful control schemes based on data quantization are proposed for complex-valued memristive neural networks (CVMNNs). Firstly, considering the finite communication resources and the interference of failures to the system, a state quantized sampled-data controller (SQSDC) is designed for CVMNNs. Next, taking the interference of gain fluctuations into account, a non-fragile sampled-data control (SDC) law is proposed for CVMNNs in the framework of data quantification. In order to full capture more inner sampling information, a newly Lyapunov-Krasovskii function (LKF) is constructed on the basis of the proposed triple integral inequality. After that, in the framework of taking full advantage of the property of Bessel-Legendre inequality, a time-dependent discontinuous LKF (TDDLKF) is proposed for CVMNNs with SQSDC. Based on the useful LKF, several stability criteria are established. Finally, the numerical simulations are provided to substantiate the validity and less conservatism of the proposed schemes.

Further Studies on Sampled-Data Consensus of Multi-Agent Systems With Communication Delays

To lower the computational burden and weaken the impact of uncertainties of controllers on system stability, a novel nonfragile sampled-data control scheme is presented for multi-agent systems (MASs) with communication delays. A modified looped-functional, where some matrices are not required to be positive, is introduced by fully utilizing the state information of some intervals. Furthermore, based on the constructed Lyapunov functional, together with the free weighting matrix method, the improved sufficient conditions for consensus of the MASs are derived in the form of linear matrix inequalities (LMIs). Finally, two numerical simulations are used to illustrate the better performance of the proposed methods than other existing literature.

Nonfragile Synchronization of Semi-Markovian Jumping Neural Networks with Time Delays via Sampled-Data Control and Application to Chaotic Systems

This study discusses the synchronization problem for delayed neural networks with semi-Markovian jumping parameters. With the support of Jenson’s inequality and Writinger-based integral inequality, a suitable Lyapunov–Krasovskii functional was constructed, and a synchronization criterion for the considered system was derived in the form of LMIs. In order to cope with system uncertainties, the nonfragile controller was taken into account. Also, sampled-data controller is used to improve the effectiveness of the bandwidth usage. In order to achieve the benefits of both control techniques, the nonfragile sampled-data controller was considered for synchronization of semi-Markovian jumping neural networks and to assure that the error system is asymptotically stable. At last, numerical simulations are exhibited to validate the proposed technique.

Mixed $$H_{\infty }$$ and passivity analysis for T-S fuzzy system with non-fragile memory sampled-data control via augment Lyapunov–Krasovskii functional

Mixed $$H_{\infty }$$ and passivity analysis for T-S fuzzy system with non-fragile memory sampled-data control via augment Lyapunov–Krasovskii functional

Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control

This paper investigates the stability and stabilizability of complex-valued memristive neural networks (CVMNNs) with random time-varying delays via non-fragile sampled-data control. Taking the influence of gain fluctuations into account, a non-fragile sampled-data controller is designed for CVMNNs. Compared with the existing control schemes, the one here is more applicable and can effectively save the communication resources. The assumption on activation functions of CVMNNs is relaxed by only needing the complex-valued activation functions satisfying the Lipschitz condition. By constructing a suitable Lyapunov–Krasovskii functional (LKF), new stability and stabilizability criteria are derived for CVMNNs. Different from the existing results with the maximum absolute values of memristive connection weights, our ones are based on the average values of the maximum and minimum of the memristive connection weights. Finally, numerical simulations are given to validate the effectiveness of the theoretical results.

Exponential stabilization and non-fragile sampled-date dissipative control for uncertain time-varying delay T-S fuzzy systems with state quantization

In this paper, the problem of exponential dissipation stability of T-S fuzzy system with state quantization is studied by using non-fragile sampled-data control. First, A Lyapunov-Krasovskii function containing all sampled-data and quantization information is constructed. Second, the better results can be obtained by using the integral inequality and the Newton Leibniz formula. In addition, a dissipative controller for non-fragile sampled-data is designed. Finally, The reasonable examples illustrate the significant improvement and value of this paper.

Sampled-data exponential synchronization of time-delay neural networks subject to random controller gain perturbations

• A non-fragile sampled-data control method is used to study the exponential synchronization of neural networks with mixed delays. • A sufficient exponential stability condition is developed by using a new two-sided looped Lyapunov functional. • A design scheme of the non-fragile sampled-data controller is proposed by using some decoupling techniques. In this paper, a non-fragile sampled-data control method is used to investigate the exponential synchronization of neural networks with discrete and distributed delays. The occurrence of controller gain perturbations is assumed to be random, which is described by a stochastic variable with the Bernoulli distribution. An extended two-sided looped Lyapunov functional is constructed, which efficiently utilizes available state information of the sampled instants. By using the two-sided looped Lyapunov functional and introducing suitable free weighting matrices, a sufficient condition is derived under which the resulting synchronization-error system is exponentially stable. Then, a design scheme of the non-fragile sampled-data controller is proposed with the aid of some decoupling techniques. At last, a numerical example is provided to illustrate the effectiveness and superiority of the proposed sampled-data control method.

Non-fragile synchronization of complex dynamical networks with hybrid delays and stochastic disturbance via sampled-data control

This paper focuses on the global synchronization issue for a class of the complex dynamical networks (CDNs) with hybrid delays and stochastic disturbance. The hybrid delays consider two forms: discrete-time coupling delays and distributed coupling delays. The non-fragile sampled-data control protocol, which is allowed to norm-bounded uncertainty, is considered for the first time in this paper. Next, by applying a new augmented Lyapunov–Krasovskii functional (LKF), with the aid of the convex combination method and the stochastic analysis technique, a less conservative condition is derived to ensure that the considered CDNs can achieve the global synchronization with hybrid delays and stochastic disturbance under a non-fragile sampled-data control strategy. Finally, there exists three simulation examples to verify the effectiveness and advantages of the analytical results.

Extended dissipativity-based non-fragile sampled-data control of fuzzy Markovian jump systems with incomplete transition rates

This article addresses the topic of extended dissipative analysis and non-fragile sampled-data control for fuzzy markovian jump systems with incomplete transition rates, where the extended dissipative analysis unifies the L2−L∞,H∞, passivity, (Q, S, R)-dissipativity performance in a framework. First, by making the best of the information in the sampling interval, an appropriate closed-loop function dependent sampling interval is constructed. Then, with the addition of free-weighting matrices approach and improved integral inequality technique, sufficient conditions are obtained to ensure the considered systems are stochastically stable and extended dissipative, respectively. Furthermore, the corresponding two form of non-fragile mode-dependent sampled-data controllers are also designed. Finally, a simulation example is offered to verify the feasibility of the results.

Finite‐time sampled‐data fuzzy control for a non‐linear system using passivity and passification approaches and its application

This study addresses the finite-time non-fragile sampled-data control for a Takagi–Sugeno (T–S) fuzzy system with passivity and passification approaches. Firstly, a new finite-time boundedness (FTB) condition is acquired for the considered system by using a Lyapunov–Krasovskii functional and inequality approaches. Secondly, finite-time passivity (FTP) conditions are achieved to ensure that the T–S fuzzy system is not only FTB but also finding passivity performance index. Moreover, under the FTP conditions, the non-fragile sampled-data controller is designed for linear matrix inequalities to achieve the stability conditions of the considered system. Finally, the corresponding simulation results for the application examples related to a permanent magnet synchronous motor system, truck trailer system, and Lorenz system are applied to verify the performance of the proposed design method.