This study focuses on the design of a security-guaranteed observer-based proportional-integral-derivative (PID) controller for discrete-time delayed Takagi-Sugeno (T-S) fuzzy systems subject to stochastic communication protocol (SCP) and false data injection (FDI) attacks. To resolve communication contention, the sensor-to-observer transmission over the constrained channel is coordinated via a SCP, whose switching is described by a Markov chain. Sufficient conditions are derived for constructing mode-dependent observer and controller that guarantee input-to-state stability (ISS) of the augmented dynamics while meeting prescribed security indices. By combining an LMI-based orthogonal decomposition approach with free matrices, the observer and controller gains are systematically computed. Finally, comprehensive simulation studies validate the theoretical advances and confirm the superior robustness and security performance of the proposed scheme under adversarial cyber-physical conditions.

This paper addresses the fault estimation and compensation control problem for nonlinear stochastic multi-delays systems with multi-source faults. The T–S fuzzy model is used to describe the nonlinearity of the system. By assuming that each stochastic delay is independent and follows the Bernoulli distribution to describe the stochastic multi-delays possibly caused by sensor/actuator faults, the targeted fault observers with historical information are introduced to estimate the sensor/actuator faults respectively, so that the designed fault-tolerant compensation control (FTCC) method can ensure the stochastic stability andH∞control performance for the closed-loop FTCC system. The sufficient design conditions for the fuzzy observer and the fault-tolerant compensation controller are obtained through the application of Lyapunov-Krasovskii stability theory and techniques. Finally, the effectiveness of the designed algorithm is verified through a common automation control system.

This article focused on the investigation of boundary dissipative control for a class of nonlinear parabolic partial differential equation (PPDE). By employing the Takagi–Sugeno (T–S) fuzzy modeling framework, the nonlinear system is characterized as a category of linear PPDE. An output-based nonparallel distributed fuzzy state observer is developed to estimate the unmeasured system state. Dynamic errors with undetermined parameters for the PPDE subject to appropriate boundary conditions are presented. To overcome performance limitations of traditional controllers in managing systems with spatially distributed characteristics, a boundary-based nonparallel distributed dynamic control scheme incorporating time delays and actuator failures is developed, building upon the established observer framework. Meanwhile, to enhance the efficiency of network resources, signals of the measurement output, observer state, and controller state are quantized through a specific dynamic quantizer scheme. Then, the finite-time dissipative performance of the fuzzy system is rigorously analyzed through the constructed Lyapunov functionals. Conditions and parameters design algorithm for the fuzzy closed-loop systems are established to ensure the specified control performance. In addition, a method involving arbitrary given matrices is employed to decouple the established nonlinear conditions. Finally, the efficacy of the proposed dissipative control strategy is validated through simulation results of the hot strip mill cooling process.

This paper concentrates on the real-time reachable set synthesis of heterogeneous nonlinear singular multiagent systems with uncertain parameters based on adaptive fault-tolerant control under non-zero initial conditions. A proportional and differential-like adaptive fault-tolerant control protocol is designed to respond to this problem, and a closed-loop singular multiagent system is built through this control protocol. Then a Lyapunov function suitable for the system is constructed, and it is proved that when there is a fault in the system, the error system will ultimately converge to the neighborhood near the origin. In addition, using the free-weighting matrices strategy and particular decoupling method to solve the linear matrix inequalities, the real-time reachable set boundary of the closed-loop singular multiagent control system and the gains of the designed controller are obtained. Finally, the efficacy of the presented strategy is demonstrated by practical examples of multiagent support systems and the DC motor models.Note to Practitioners—Because of the complex and changeable characteristics of a modern engineering system, it can not be operated by a single module, so the research of multiagent systems has become the mainstream trend of modern system research. In the actual project, the safety problem must be addressed. Therefore, our main research content is how to make the machine or system model run within a more accurate security range. In practical applications, some random and unpredictable events often occur, which may lead to failures in the system and make the system unable to operate properly. These behaviors may lead to unknown security problems. When we study security issues, we need to consider the occurrence of this situation. Therefore, when a fault occurs in the system, ensuring the system’s normal operation within the safe range is one of the key topics that need to be studied. Unlike ordinary systems, singular systems can more widely describe the actual system model. Therefore, the study of singular systems is indispensable in actual engineering system research. Based on the above analysis, the real-time reachable set synthesis of heterogeneous nonlinear singular multiagent systems with faults is studied, and the consensus protocol of the proportional and differential-like is designed.

In this article, an observer-based$l_{2}$–$l_\infty$performance analysis and controller design problem is proposed for a category of discrete-time nonlinear control systems. First of all, the closed-loop system with external disturbance in both state and output variables under consideration is represented as a discrete-time Takagi–Sugeno (T–S) fuzzy model and is described by the descriptor representation method. Subsequently, a new Lyapunov function is suggested that satisfies the prescribed$l_{2}$–$l_\infty$performance while ensuring stability. The application of the linear matrix inequality technique is employed to establish an analysis criterion for the$l_{2}$–$l_\infty$performance. By defining the structure of matrix variables, new design conditions of observer-based$l_{2}$–$l_\infty$controller are given. Ultimately, an instance demonstrates the effectiveness of the proposed controller design criterion.

In this article, the problem of quantified guaranteed cost fault-tolerant control for continuous-time fuzzy singular systems with sensor and actuator faults is studied. The system nonlinearity is approximated by the Takagi-Sugeno (T–S) fuzzy model. Considering the multiple signal dynamic quantization and the composite faults, based on the augmented fuzzy system and constructed dynamic system, the intermediate observer and fault-tolerant controller are constructed to make the fuzzy quantified closed-loop control system mean-square admissible and meet the expected guaranteed cost performance index. By introducing Lyapunov function and some lemmas, the sufficient design conditions of fault-tolerant controller are proposed in the light of linear matrix inequalities representations. Finally, a DC motor driving model will be given to show the efficiency of the proposed design methods.

This article addresses the quantized asynchronous filtering issue for semi-Markov switching systems described by the Takagi–Sugeno fuzzy model with multiple stochastic delays under deception attacks. First, considering communication congestion and the randomly changing environment, multiple stochastic time-varying delays are introduced via the Bernoulli distribution. Second, a mode-dependent quantizer is proposed to save the limited network bandwidth. Third, to enhance network security, the measurement output signal is assumed to suffer from deception attacks during the transmission process. Subsequently, under the influences of the above-mentioned three factors, a novel filtering error model is well constructed. Further, by applying the Lyapunov–Krasovskii stability theory and the stochastic analysis techniques, a delay-dependent stochastic stability criterion with less conservatism is given to ensure that the filtering error system is stochastically stable with a prescribed$\mathcal {H}_{\infty }$performance, where the feasible asynchronous filter gains and dynamic parameters can be obtained in a unified framework. Ultimately, two examples are employed to demonstrate the availability of the designed filter method.

The problem of$\mathfrak{L}_{2}-\mathfrak{L}_{\infty}$quantized feedback control for lateral dynamics of electric vehicles is investigated in this article, in which the nonlinear vehicle lateral is represented by the T–S fuzzy model. A novel Lyapunov function and two-step design strategy are introduced to design the controller and the quantizer, which guarantee the same$\mathfrak{L}_{2}-\mathfrak{L}_{\infty}$performance as without quantization. Compared with the existing results, the advantage of the proposed design strategy is that the coefficient can be set to change the adjustment range of the quantizer parameters, thereby reducing the conservative of the design. Finally, the co-simulation of MATLAB/Simulink and Carsim with a full-vehicle model is provided to demonstrate the effectiveness of the proposed design strategy.

The exponential stabilization of nonlinear distributed parameter systems (DPSs) with time-varying delay is studied in this article. With the employment of the Takagi–Sugeno (T–S) fuzzy model, the nonlinear DPSs are described as a fuzzy parabolic partial differential equation (PDE). Then, with the consideration of the measurement output and control input information quantized by a kind of dynamic quantizers, fuzzy static, and dynamic output feedback controllers are proposed. The time-delayed feedback controllers are also provided in the forms of static and dynamic. On the basis of the derived T–S fuzzy PDE model, Lyapunov-based control design strategies are proposed with respect to the effect of quantization. Moreover, by constructing appropriate Lyapunov functionals, sufficient conditions for designing the output feedback control gains and the quantizers' adjusting parameters, which guarantee the exponential stability of the fuzzy closed-loop system are provided in terms of standard linear matrix inequalities. Finally, we explore the output feedback controllers into the FitzHugh–Nagumo equation and Fisher equation as applications. The efficiency of the design strategies is indicated through the simulation results.

This article investigates the problem of event-based output feedback tracking control for discrete-time nonlinear networked systems with dynamic quantization. The Takagi–Sugeno (T–S) fuzzy systems theory is utilized to approximate the investigated nonlinear systems. Three general dynamic quantizers and an improved asynchronous event-triggering communication scheme are carried out to comprehensively decrease the amount of data in the communication of network and realize the rational utilization of limited communication resources. The objective is to design an event-based static output feedback tracking controller such that, in the presence of dynamic quantization, the closed-loop system can be asymptotically stabilized, and the tracking error achieves the predefined tracking performance. Moreover, the parameters for the desired dynamic quantizers and tracking controller can be obtained simultaneously by solving a set of linear matrix inequalities. Finally, the simulation responses are provided to demonstrate the validity of the proposed tracking control strategy.

This article proposes an induced$\mathcal {L}_\infty$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$\mathcal {L}_\infty$performance index. The sufficient conditions of stability with induced$\mathcal {L}_\infty$control performance are given as the basis of controller design. Furthermore, the induced$\mathcal {L}_\infty$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.

The output regulation of nonlinear distributed parameter systems (DPSs) with time-varying delay is studied in this article. By employing the Takagi–Sugeno (T–S) fuzzy model, the nonlinear time-delay DPSs are described as a fuzzy partial differential equation. To deal with the limited capability of communication channels, the state information and output regulation error are quantized by a kind of dynamic quantizer. The actuator failures which occur frequently in practice are considered in two cases: to be unknown and to follow the Bernoulli stochastic distribution. Then, the boundary-control-based output regulation controllers are designed based on the derived T–S fuzzy model. And fuzzy controller design strategies are proposed to ensure the system state and the output regulation error are finite-time bounded. By constructing appropriate Lyapunov functionals, sufficient conditions for the control gains and the quantizer’s adjusting parameters are provided in forms of linear matrix inequalities. Finally, an application of the output regulation strategies into the cooling process in a hot strip mill is explored. The feasibility and effectiveness of the design strategies are indicated through the simulation results.

In this article, an induced$\mathcal L_{\infty }$synchronization controller design problem is proposed for the complex network with periodic switched coefficients under the dynamic quantization scheme. A novel periodic dynamic quantization scheme is utilized to handle the transmitted control signal to avoid the undesired networked problem, in which, the variables to be designed are introduced as parameter-dependent variables corresponding to the periodic switched coefficients of the complex network to increase the flexibility. Then, by using a robust-model-based approach, the dynamics of the synchronization error are modeled by a special uncertain system. The main purpose of the problem addressed is to design a synchronization controller, such that the complex network is exponentially synchronized with an induced$\mathcal L_{\infty }$performance. Sufficient conditions are established to guarantee the synchronization task. Next, the design conditions of both of synchronization controller and parameter-dependent variables related the dynamic adjustable parameter are characterized by the convex problem. An example demonstrates the proposed controller design criteria and the dynamic quantization scheme.

This article studies the energy-to-peak filter design problem for the continuous-time nonlinear singular system. A Takagi–Sugeno fuzzy model is constructed to represent the nonlinear plant. The article focuses on the fuzzy filter design for singular system, such that the formulated filtering error system is admissible (regular, impulse-free, and stable) and ensures the corresponding filtering performance in the singular system. By introducing a novel Lyapunov function, design conditions of the fuzzy filter for the continuous-time singular system are proposed in the light of linear matrix inequalities representations. Finally, a practical example and the relevant simulation results are provided to demonstrate the effectiveness and feasibility of the proposed energy-to-peak filter design approach.

In this article, the resilient$\mathcal H_\infty$filter design problem for continuous-time nonlinear systems is addressed. A Takagi–Sugeno fuzzy model with norm-bounded uncertainties is used to represent the nonlinear plant. Meanwhile, the fuzzy filter to be designed is assumed to have gain variations. A useful matrix inequality decoupling approach is proposed to separate the product terms with different types of uncertainties. Then, the resulting design condition of the resilient$\mathcal H_\infty$filter is described by strict linear matrix inequalities. Compared with the existing fuzzy resilient filtering results, the proposed design method shows a strong advantage of reducing design conservatism. Finally, a simulation example is provided to demonstrate the feasibility and the advantage of the proposed design method.

In this article, we study the robust quantized feedback control problem for nonlinear discrete-time systems that are described by Takagi–Sugeno (T–S) fuzzy model with norm-bounded uncertainties. The dynamic quantizer composed of a dynamic parameter and a static quantizer is considered to quantize the control input signal. An improved two-step approach to design controller and dynamic quantizer for T–S fuzzy system is proposed based on the LMI technique. In the first step, a robust controller is designed to guarantee that the quantized fuzzy closed-loop system with norm-bounded uncertainties is asymptotically stable with prescribed$\mathcal {H}_{\infty }$performance. Then, the parameter-dependent (membership function) scalar variable is obtained to determine the dynamic quantizer’s parameter in the second step. Finally, the simulation result of truck–trailer system is presented to validate the effectiveness and feasibility of the proposed two-step design approach.

This article proposes a robust$\mathcal {H}_\infty $output feedback control strategy to improve vehicle lateral stability through active front wheel steering with quantization error, transmission delay and data dropouts. Since vehicle lateral dynamics presents inherently uncertain challenges, the polytopic technique is used and the variation of tire cornering stiffness is compensated via norm-bounded uncertainty. Meanwhile, considering that the measurement outputs are inevitably suffer from network-induced constrains, i.e. the quantization error, transmission delay and data dropouts, which degrade the stability and handling performance of vehicle, a robust gain-scheduling$\mathcal {H}_\infty $output feedback controller design strategy is proposed and the stability conditions are presented in the form of linear matrix inequalities that are derived from Lyapunov asymptotic stability and quadratic$\mathcal {H}_\infty $performance. Finally, two simulation cases are carried out with MATLAB/Simulink-Carsim to validate the effectiveness of the proposed method.

In this article, we investigate the parabolic partial differential equations (PDEs) systems with Neumann boundary conditions via the Takagi–Sugeno (T–S) fuzzy model. On the basis of the obtained T–S fuzzy PDE model, a novel fuzzy state controller which is associated with the boundary state of position and the mean value coefficient matrix derived through the mean value theorem of integral is designed to analyze the asymptotic stability of the parabolic PDE system. Without sampling the nonlinear parameter of the system, new stability conditions are deduced in the form of linear matrix inequalities (LMIs). Moreover, compared with the novel fuzzy state controller, more conservative conditions based on another fuzzy state controller are also provided. Finally, we explore the state-feedback controller into the Fisher equation as an application. Simulation results show that the proposed method is effective.

This article focuses on the design problem of fault detection (FD) observer for a class of continuous-time nonlinear systems. The$\mathcal {H_{-}}$index is employed to measure the worst cast fault sensitivity. First, the nonlinear systems are represented by the Takagi–Sugeno (T–S) fuzzy dynamic models. Then, a novel fuzzy Lyapunov function (FLF) analyzing approach is proposed to reduce the conservatism in stability analysis and$\mathcal {H_{-}}$index estimation for the T–S fuzzy model. In particular, employing this new type of the FLF, the stability analysis of the T–S fuzzy systems perfectly overcomes the difficulty of evaluating the upper bound of the time derivative of the membership function. Based on the descriptor system approach, some new sufficient conditions on the existence of fuzzy FD observers are obtained in terms of linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the effectiveness of the proposed method.

This article investigates the problem ofH∞dynamic output feedback control for a class of discrete-time nonlinear systems with quantization and stochastic communication protocol (SCP) via Takagi-Sugeno rules. A fuzzy Markov jump model is used to describe the output feedback controller with the measurement output signal quantized by the dynamic quantization strategy and the SCP scheduling behavior for the quantized signal transferring to the controller described via a Markov chain. The purpose of the addressed problem is to design a dynamic output feedback controller such that stability andH∞performance for the closed-loop system are guaranteed. For this purpose, the corresponding design conditions for the output feedback controller and the dynamic quantization parameters are presented in terms of solutions to a set of linear matrix inequalities. Finally, two simulation examples are given to prove the effectiveness of the proposed design method.

In this article, theH∞static output feedback tracking control problem is studied for discrete-time nonlinear networked systems subject to quantization effects and asynchronous event-triggered constraints. The Takagi-Sugeno (T-S) fuzzy model is utilized to represent the investigated nonlinear networked systems. A novel asynchronous event-triggered strategy is given to reduce the network communication burdens in both communication channels from the plant to the controller and from the reference model to the controller. The objective of this article is to design a quantized event-triggered tracking controller such that the resulting system is asymptotically stable and the givenH∞tracking performance is guaranteed. The sufficient design conditions for the tracking controller are formulated in the form of the linear matrix inequalities (LMIs). Furthermore, a simulation example will be utilized to show the effectiveness of the developed design strategy.

This paper studies the quantized state-feedback energy-to-peak control problem for networked nonlinear system with data dropout, in which the T-S fuzzy technique is adopted to deal with nonlinear challenge and a stochastic variable satisfying the Bernoulli random binary distribution is used to model this data dropout phenomenon. A novel two-step design strategy, based on linear matrix inequality technique, is proposed for the design of controller and dynamic quantizer such that the closed-loop system being stochastically stable with the prescribed energy-to-peak performance. Finally, an application is presented to validate the effectiveness of the proposed design method.

This paper investigates the feedback guaranteed cost control problem for a class of uncertain discrete-time systems with input quantization. The plant with polytope type uncertainties is considered. The input signal will be quantized by a dynamic quantizer before it is transmitted from the controller to the system under digital communication channels. First, a design method of the feedback control for the quantized closed-loop system is given to attain the same guaranteed cost performance as the one without signal quantization. Then, by introducing some useful auxiliary scalars, the dynamic parameter of the quantizer is also designed effectively so that the desired performance level can be achieved. A simulation example is provided to show the effectiveness of the proposed design method.

This paper studies the estimation problem for a class of nonlinear tunnel diode circuits with parameter perturbation. The dynamics of the nonlinear circuit is approximated by the Takagi-Sugeno fuzzy model with linear fractional parametric uncertainties. Considering the fuzzy filter with gain uncertainties, a filtering error system can be gotten naturally. The concentration of this paper lies in deriving the design conditions for the desired resilient filter such that the filtering error system preserves asymptotic stability as well as the L∞-norm of the transfer function from the external disturbance input to the filtering error output is below a specified bound. The filter design conditions for guaranteeing the prescribed peak-to-peak performance of the filtering error system are provided by a set of linear matrix inequalities. Finally, a simulation result is presented to illustrate the validity and effectiveness of the proposed filtering design for the parameter-controlled tunnel diode circuit.

In this paper, the peak-to-peak filtering problem is studied for a class of networked nonlinear systems. The nonlinear physical plant is represented by the Takagi-Sugeno fuzzy system. Assume that the data packet dropout phenomenon occurs when the measurement output signal and the performance output signal of the nonlinear systems are transmitted by the digital communication channel. Two stochastic variables satisfying the Bernoulli random binary distribution are used to model this phenomenon. The motive of this paper is to design a peak-to-peak filter such that the filtering error system is stochastically stable and the prescribed peak-to-peak performance index is guaranteed. The developed theoretical results for designing a peak-to-peak filter are expressed in the form of linear matrix inequalities. Finally, a simulation example is presented to illustrate the validity of theoretical analysis.

This paper investigates the output feedback H∞control problem for a class of continuous-time nonlinear systems with both output and input quantization. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model. The measurement output and the control input signals will be quantized by dynamic quantizers. The attention of this paper is focused on the design of the output feedback controller and quantizers such that the fuzzy closed-loop system is asymptotically stable and also achieves a prescribed H∞noise attenuation level with respect to the effect of quantization. In the presence of quantization, two novel H∞performance analysis criteria are presented for the considered fuzzy systems based on the descriptor representation approach. Then, sufficient conditions for the existence of the feedback H∞controller gain and the quantizers' dynamic parameters are expressed in terms of linear matrix inequalities. An adjusting scheme on quantizers' parameters is proposed for the fuzzy system. Simulation examples are provided to show the effectiveness of the proposed design method.method.

This paper investigates the peak-to-peak filtering problem for a class of networked nonlinear dc motor systems with quantization. The nonlinear dc motor system is modeled by a Takagi-Sugeno (T-S) fuzzy model. Consider that the measurement output signal and the performance output signal of the system are quantized by two static quantizers before being transmitted by the digital communication channel, respectively. Attention is focused on the design of a peak-to-peak filter such that the filtering error system is asymptotically stable and satisfies the prescribed peak-to-peak filtering performance index. Sufficient conditions for such a peak-to-peak filter are expressed in the form of linear matrix inequalities. Finally, an illustrative simulation is given to show the effectiveness of the proposed approach.

This paper investigates the problem of output feedback control for discrete-time systems with two quantized signals in measurement output and control input. Since the measurement output and control input are quantized by general quantizers before they are passed to the controller and the system, the closed-loop system will include the quantization error terms, which might lead to that the performance of the closed-loop system is not guaranteed. For this purpose, this paper proposes a novel quantized output control strategy such that the closed-loop system is asymptotically stable or satisfies the prescribed H∞performance. The corresponding design conditions for the output feedback controllers and the quantizers' dynamic parameters are presented in terms of solutions to a set of linear matrix inequalities. Finally, a simulation example is given to prove the effectiveness of the proposed design method.

In this paper, the generalized H2filter design problems are addressed for a class of nonlinear discrete-time systems with measurement quantization. The considered nonlinear system is represented by Takagi-Sugeno fuzzy model and the system measurement output is quantized by a dynamic quantizer constituted by a static quantizer and a dynamic parameter before it is transmitted to the filter. The attention is focused on the design of both fulland reduced-order filters and the quantizer dynamic parameter such that the quantized filtering error systems are asymptotically stable with prescribed generalized H2performances. Superior to existing results on the quantized filtering design, the proposed one is given under a unified linear matrix inequality (LMI) characterization, it is shown that the design problem can be solved if the LMIs conditions are feasible. Finally, simulation examples will be exploited to illustrate the effectiveness of the developed quantized generalized H2filtering methods.

The problem of resilient energy-to-peak filtering for a class of uncertain continuous-time nonlinear systems is investigated in this paper. A Takagi-Sugeno fuzzy model with norm-bounded uncertainties is used to represent the nonlinear plant. Attention is focused on the design of an energy-to-peak filter such that the filtering error system is asymptotically stable and the prescribed energy-to-peak filtering performance is guaranteed, where the designed filter is assumed to have additive gain variations. The proposed design is aimed at all filter matrices with gain variations, which improves the existing results on resilient energy-to-peak filtering for continuous-time systems. A simulation example is provided to show the effectiveness of the proposed methods.

This paper is concerned with theH∞filtering problem for continuous-time Takagi-Sugeno (T-S) fuzzy systems. Different from existing results for fuzzyH∞filtering, the proposed ones are toward uncertain fuzzy systems with linear fractional parametric uncertainties. Attention is focused on the design of a fuzzy filter such that the filtering error system preserves a prescribedH∞performance, where the filter to be designed is assumed to have gain variations. By a descriptor representation approach, two sufficient conditions for theH∞filter design are proposed in terms of linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of the desired filter is given. A simulation example will be given to show the efficiency of the proposed methods.