This article delves into an optimal fixed-time tracking control scheme for human-in-the-loop (HiTL) multiagent systems (MASs) against actuator faults. For adapting various complex environments, an HiTL optimal control protocol is developed based on the simplified optimal control. Furthermore, the synchronization error containing the leader input is embedded into the cost function, which enables the achievement of the optimal control objective and ensures the execution of the tracking control under the HiTL control. By adding exponential terms, a novel reinforcement learning (RL) algorithm satisfying the fixed-time form is proposed to attain the optimal fixed-time controller, which prompts the convergence rate of system signals while ensuring minimum energy consumption effectively. Meanwhile, actuator faults are considered and compensated in the controller design process to attain exceptional system performance. Consequently, the presented optimal control scheme ensures that all signals of the closed-loop system maintain bounded in a fixed time. The simulation results verify the feasibility of the presented control method.

The growing number of agents and frequent interactions in multiagent systems (MASs) increase the risk of excessive data transmission and network attacks. This article investigates intermittent adaptive resilient bipartite containment control for stochastic MASs under multipoint attacks. Multipoint attacks can compromise information in cooperation–competition networks, and stochastic disturbances with unknown statistical properties are almost surely nondifferentiable. To address these challenges and reduce the communication burden associated with high data transmission rates, a novel second-order event-triggered observer with feedforward compensation is proposed. Furthermore, an improved dynamic memory event-triggered mechanism (DMETM) is developed to reduce redundant signal transmissions by leveraging historical data. Unlike existing DMETMs, the proposed scheme eliminates the need to compute the supremum of the dynamic variable. The results demonstrate that the closed-loop system is bounded in practically mean square. The feasibility and superiority of the designed method are proved through simulation experiments.

In this paper, the dynamic event-driven optimal control problem is investigated for a class of continuous-time nonlinear systems subject to asymmetric input constraints in the framework of nonzero-sum (NZS) games. Initially, by constructing a modified value function, the respective asymmetric input constraint requirements of the controllers involved in the NZS games are successfully satisfied. Then, based on the Bellman’s optimality principle, the N-coupled Hamilton-Jacobi equations are derived for the N-player NZS games. After that, the adaptive dynamic programming (ADP) method is employed to seek for the optimal control policies, in which the simpler single critic neural network structure, instead of the dual network structure of actor-critic in the typical ADP algorithm, is applied. Furthermore, an improved critic network weight updating law is proposed to ensure the stability of the closed-loop system without a hard-to-find initial admissible control scheme. In addition, in order to reduce the update frequency of the controllers to a greater extent, a dynamic event-driven mechanism with adjustable threshold is developed. Finally, a simulation example is given to demonstrate the validity of the developed event-driven control scheme. Note to Practitioners—This paper aims to address the NZS games problem for a category of multi-player continuous-time nonlinear systems featuring multiple input constraints. The applicability of this approach can be widely extended to practical domains, including control applications for reconfigurable robot systems, networked communication systems, etc. The majority of researches on multi-player NZS games problem are focused on the impact of symmetric input constraints. Especially under the premise of ensuring controller optimality, the challenge lies in how to ensure effective control functionality while subjecting the controller to asymmetric constraints. Furthermore, the existing ADP algorithms often depend on an initial admissible control, significantly elevating the implementation difficulty of control solutions in practical applications. To address these challenges, an improved ADP algorithm is developed for input-constrained nonlinear systems within a NZS game framework. This method not only guarantees that the optimal controllers under asymmetric constraints can stabilize all signals, but also avoids the search for challenging-to-find initial admissible controls, thus streamlining the control implementation process.

This article investigates the event-triggered$\mathcal {H}_{\infty }$filtering problem for interval type-2 fuzzy networked control systems with data loss. The goal of this article is to design an event-triggered fuzzy filter with adjustable gains to further improve the$\mathcal {H}_{\infty }$performance of the filtering error system (FES) while saving communication resources. First, an adaptive event-triggered mechanism (ETM) that utilizes the average filtering error to adaptively adjust the triggered threshold is proposed to mitigate network congestion. In contrast to the existing filtering achievements, the proposed adaptive ETM considers the impact of filtering error on data transmission which can achieve the effect of saving more communication resources. Then, an interval stability theory is applied to adjust the filter gains for achieving the purposes of changing the convergence speed of the FES and improving the$\mathcal {H}_{\infty }$performance. Moreover, under the conditions of imperfect communication links, sufficient criteria for designing the$\mathcal {H}_{\infty }$filter with adjustable gains are obtained based on the interval stability theory and linear matrix inequalities technique. Finally, the simulation results are utilized for demonstrating the feasibility of the proposed method.

This paper considers the prescribed performance-based optimal formation control problem for unmanned surface vehicles with position constraints and yaw angle time-varying partial constraints while avoiding collisions and maintaining connectivity. To be more specific, prescribed-time performance constraints are imposed on the position tracking errors between each vehicle and its leader. Then, the prescribed performance-based optimal formation control strategy is developed to guarantee that each vehicle achieves collision-free formation control while maintaining connectivity, as well as the prescribed transient and steady performance on the position tracking errors. Inspired by the prescribed performance control, an improved asymmetric barrier function with prescribed performance is provided to ensure that the yaw angle errors satisfy the prescribed performance constraints. Eventually, theoretical analysis demonstrates that the optimal formation control scheme can produce position tracking errors that converge to a prescribed arbitrarily small region within a prescribed time interval, along with the yaw angle that adheres to the time-varying partial constraints, subject to optimal cost with limited communication ranges and collision avoidance constraints. Simulation results and comprehensive comparisons show extraordinary effectiveness and superiority.

This paper focuses on the predefined-time adaptive neural tracking control problem for nonlinear multiagent systems (MASs). In contrast to the existing results of the predefined-time control methods, this paper introduces a lemma for achieving predefined-time stability within the framework of backstepping, and the primary distinguishing feature is the ability to predefine the convergence time according to user specifications and the controller design process being influenced by a singular parameter. Meanwhile, a numerical example is presented by using the proposed lemma such that the convergence performance can be ensured by the user practical specification. Moreover, by using the neural networks (NNs) and the finite time differentiators, an adaptive approach to predefined-time tracking control is presented for nonlinear MASs. This method ensures the predefined-time stability of all signals within the MASs, while also enabling the followers’ outputs to accurately track the desired trajectory with the predefined time. The effectiveness and merits of the proposed scheme are substantiated through simulation results.Note to Practitioners— This paper aims to address the predefined-time control problem for MASs, which can be widely used in practice, such as vehicular platoon systems control, teleoperation systems control, etc. The existing predefined-time methods only guarantee system convergence within the predefined-time interval, and achieving predefined-time convergence with an exact convergence time$t$remains a challenge. Moreover, the existing predefined-time methods contain many control parameters, which complicates the process of the parameter tuning. To address the aforementioned challenges, a predefined-time adaptive neural control method for MASs is developed, which can guarantee that all signals within MASs are predefined-time stable while enabling the followers to accurately track the desired trajectory with predefined time. Moreover, only one parameter and a pair of the finite time differentiators designed constants are involved in the controller design process, which simplifies the process of the parameter tuning.

This article proposes the two-layer asynchronous control scheme for a class of networked nonlinear jump systems. For the constructed system in a network environment, the data transmission may suffer from many restrictions, such as incomplete acceptable mode information and transition information, nonlinearity of system and inadequate bandwidth resources, etc. Then, the two-layer asynchronous controller is developed to stabilize the plant constructed by Takagi-Sugeno (T-S) fuzzy method and semi-Markov theory (SMT). Herein, the hidden semi-Markov process with time-varying emission probability is introduced to establish the relation between the system modes and the controller modes, in which the interval segmentation method is presented to deal with this time-varying probability. Compared with some published results, this method can make full use of the transition rate information, which may lead to the reduction of conservatism in the proposed asynchronous control design. At the same time, the limited bandwidth problem in the communication channel is addressed by introducing the bilateral quantization strategy, and the new sufficient conditions are derived on the stochastic stability of the nonlinear jump system with/without incomplete transition and sojourn-time information. Finally, the numerical simulation examples about DC motor illustrate the effectiveness and the feasibility of the proposed approach.

In this paper, a self-triggered fuzzy adaptive switched control strategy is proposed to address the synchronous tracking issue in switched stochastic multiagent systems (MASs) based on mode-dependent average dwell-time (MDADT) method. Firstly, a synchronous slow switching mechanism is considered in switched stochastic MASs and realized through a class of designed switching signals under MDADT property. By utilizing the information of both specific agents under switching dynamics and observers with switching features, the synchronous switching signals are designed, which reduces the design complexity. Then, a switched state observer via a switching-related output mask is proposed. The information of agents and their preserved neighbors is utilized to construct the observer and the observation performance of states is improved. Moreover, a modified self-triggered mechanism is designed to improve control performance via proposing auxiliary function. Finally, by analysing the relationship between the synchronous switching problem and the different switching features of the followers, the synchronous slow switching mechanism based on MDADT is obtained. Meanwhile, the designed self-triggered controller can guarantee that all signals of the closed-loop system are ultimately bounded under the switching signals. The effectiveness of the designed control method can be verified by some simulation results.

This study explored the issue of decentralized adaptive event-triggered neural network (NN) control for nonlinear interconnected large-scale systems (LSSs) subjected to unknown measurement sensitivity and nonconstant control gains. Due to the impact of unknown measurement sensitivity, the real states of LSSs cannot be directly utilized. To overcome this difficulty, an effective adaptive feedback control scheme was developed. Subsequently, NNs were exploited to address the nonlinear terms and unknown nonconstant control gains. A modified first-order compensation system was developed to enhance the control performance in the presence of saturation nonlinearity. Furthermore, a significant dynamic event-triggered control (DETC) protocol was developed based on the saturation controller and measurement error, which reduced the number of controller updates. According to the Lyapunov stability theory, the proposed DETC-based decentralized adaptive protocol demonstrated that all signals were semiglobally uniformly ultimately bounded. The simulation examples illustrate the validity of the presented control protocol.

To solve the tracking control problem, this article proposes a detect-switch-compensate mechanism based on fault-tolerant control strategy. Due to the existing challenge of nonlinear faults about occurrence time being unknown, a fault observer is constructed to recognize when faults occur, and the controller is activated to the fault compensate mode. Based on the above analysis, compared with the method of continuous compensation, the proposed detect-switch-compensate algorithm which uses the discontinuous compensation method having the advantage of high efficiency. Furthermore, the fault observer also solves the problem of unmeasured states. Meanwhile, both the normal tracking error and the prediction error caused by the predictor technology are considered to the feedback compensation, which prevents the system being unstable due to the large prediction error. On the basis of the Lyapunov stability, the stability of the closed-loop system is proven. A simulation example is supplied to elicit the availability of the presented methodology.

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.

In bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems (MASs), stochastic disturbances and actuator faults are regarded as essential factors that hamper effective controller formulation and tracking precision improvement. To address these difficulties, we design an improved finite-time performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative control scheme to achieve finite-time robust precision BCT tasks for nonlinear MASs. The parameter selection range of the improved FTPF is relaxed, which renders systems to achieve better transient performance. Benefitting from the stochastic Lyapunov stability theory, it is shown that all signals of systems are semiglobal uniformly ultimately bounded in probability, and bipartite consensus errors can satisfy the arbitrary precision with probability in the predefined time. Finally, to verify its effectiveness, the proposed control scheme is applied to BCT tasks of a group of vehicles, which manifests anticipated control performance under various uncertainties.

This article investigates the adaptive performance guaranteed tracking control problem for multiagent systems (MASs) with power integrators and measurement sensitivity. Different from the structural characteristics of existing results, the dynamic of each agent is a power exponential function. A method called adding a power integrator technique is introduced to guarantee that the consensus is achieved of the MASs with power integrators. Different from existing prescribed performance tracking control results for MASs, a new performance guaranteed control approach is proposed in this article, which can guarantee that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. By utilizing the Nussbaum gain technique and neural networks, a novel control scheme is proposed to solve the unknown measurement sensitivity on the sensor, which successfully relaxes the restrictive condition that the unknown measurement sensitivity must be within a specific range. Based on the Lyapunov functional method, it is proven that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. Finally, a simulation example is proposed to verify the availability of the control strategy.

This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.

In the fault-tolerant control (FTC) tasks of nonstrict-feedback nonlinear systems, unmeasurable states, disturbance, and actuator faults are recognized as the main factors that obstacle the effective controller design and, thus, the tracking performance improvement. To tackle these obstructions, a fuzzy observer is introduced to address the difficulties of the unmeasurable states involving nonstrict-feedback nonlinear systems by benefiting from the approximation property of fuzzy logic systems. Owing to the newly employed damping term in the intermediate control law being utilized to compensate for the possibly unlimited number of faults, the proposed FTC strategy is able to deal with actuator faults properly without imposing tighter requirements on the fault mechanism. To reach fast transient performance, stability of finite time is reached by exploiting the backstepping method. The investigated strategy ensures that all the responses of the systems are semiglobal practical finite-time stable. Meanwhile, the tracking error converges to a small neighborhood of the origin within finite time. In addition, to demonstrate its effectiveness, the provided approach is applied to the position tracking of a robotic system, which shows anticipated control performances in spite of various uncertainties.

This article addresses the fixed-time control problem for the constrained quarter active vehicle suspension systems (AVSSs) via an event-triggered based adaptive fuzzy fixed-time control method. The benefit of the usage of the time-varying barrier Lyapunov function is to avoid the violation of the time-varying displacement constraint so that the stability and safety of AVSSs can be guaranteed. The relative threshold based event-triggered controller is devised so as to reduce the communication burden from the controller to the actuator. In the light of fixed time theory, it is proved that both the stability and tracking performance of the closed-loop system can be obtained in fixed time. The fixed-time based event-triggered control strategy is independent of initial states of AVSSs in comparison with the existing finite-time results. Some simulation results and comparisons on a quarter-car AVSS indicate better performance in terms of feasible fixed-time control and exact trajectory tracking.

This article focuses on the$\mathcal {L}_{2}-\mathcal {L} _{\infty}/ \mathcal {H}_{\infty}$optimization control issue for a family of nonlinear plants by Takagi–Sugeno (T–S) fuzzy approach with actuator failure. First, considering unmeasurable system states, sufficient criteria for devising fuzzy imperfect premise matching dynamic output feedback controller to maintain asymptotic stability while guaranteeing a mixed performance for T–S fuzzy systems are provided. Therewith, in the light of feasible areas of dynamic output feedback controller membership functions (MFs), a new MFs online learning policy using gradient descent algorithm is proposed to learn the real-time values of MFs to acquire a better$\mathcal {L}_{2}-\mathcal {L}_{\infty}/ \mathcal {H}_{\infty}$control effect. Different from the traditional method using an imperfect premise matching scheme, under the proposed optimization algorithm, the trajectory of mixed performance index is lowered effectively. Afterward, a sufficient criterion is presented for assuring the convergence of the error of the cost function. Finally, the superiority of this online optimization learning policy is confirmed via simulations.

This article studies the issue of resilient event-triggered (RET)-based security controller design for nonlinear networked control systems (NCSs) described by interval type-2 (IT2) fuzzy models subject to nonperiodic denial of service (DoS) attacks. Under the nonperiodic DoS attacks, the state error caused by the packets loss phenomenon is transformed into an uncertain variable in the designed event-triggered condition. Then, an RET strategy based on the uncertain event-triggered variable is firstly proposed for the nonlinear NCSs. The existing results that utilized the hybrid triggered scheme have the defect of complex control structure, and most of the security compensation methods for handling the impacts caused by DoS attacks need to transmit some compensation data when the DoS attacks disappear, which may lead to large performance loss of the systems. Different from these existing results, the proposed RET strategy can transmit the necessary packets to the controller under nonperiodic DoS attacks to reduce the performance loss of the systems and a new security controller subject to the RET scheme and mismatched membership functions is designed to simplify the network control structure under DoS attacks. Finally, some simulation results are utilized to testify the advantages of the presented approach.

This article deals with the fault detection problem for a class of Takagi–Sugeno (T–S) fuzzy semi-Markov jump systems (FSMJSs) with partly unknown transition rates (PUTRs) subject to output quantization by designing a reduced-order filter. First, a more general PUTRs model is constructed to describe the situation that the information of some elements is completely unknown, where this model is affected simultaneously by PU information and time-varying parameter compared with the traditional PUTRs model. Second, we take full advantage of the reduced-order filter to address the fault detection problem for FSMJSs, in which the stochastic failure phenomenon is injected into the reduced-order filter. Besides, the logarithmic quantizer is employed to tackle the limited bandwidth problem in a communication channel. Consequently, the new sufficient conditions are developed based on the Lyapunov theory to obtain the desired reduced-order filter. Simulation results with respect to the tunnel diode circuit are provided to demonstrate the usefulness and availability of the established theoretical results.

This article investigates the quantized adaptive finite-time bipartite tracking control problem for high-order stochastic pure-feedback nonlinear multiagent systems with sensor faults and Prandtl-Ishlinskii (PI) hysteresis. Different from the existing finite-time control results, the nonlinearity of each agent is totally unknown in this article. To overcome the difficulties caused by asymmetric hysteresis quantization and PI hysteresis, a new distributed control method is proposed by adopting the adaptive compensation technique without estimating the lower bounds of parameters. Radial basis function neural networks are employed to estimate unknown nonlinear functions and solve the problem of algebraic loop caused by the pure-feedback nonlinear systems. Then, an adaptive neural-network compensation control approach is proposed to tackle the problem of sensor faults. The problem of the "explosion of complexity" caused by repeated differentiations of the virtual controller is solved by using the dynamic surface control technique. Based on the Lyapunov stability theorem, it is proved that all signals of the closed-loop systems are semiglobal practical finite-time stable in probability, and the bipartite tracking control performance is achieved. Finally, the effectiveness of the proposed control strategy is verified by some simulation results.

This article investigates the problem of event-based decentralized adaptive fuzzy output-feedback finite-time control for the large-scale nonlinear systems. The full-state tracking error constraints, unmeasured states, and external disturbances are simultaneously considered in the controlled systems. The unknown auxiliary functions are modeled by using fuzzy logic systems, and a state observer is established to estimate unmeasured states. By taking a new error transformation method based on prescribed performance functions and constructing corresponding barrier Lyapunov functions, the predefined system error dynamic performance is ensured. Then, on the basis of the event-triggered control technique and the backstepping recursive design technique, a new event-based adaptive fuzzy nonsingular finite-time control strategy is proposed, and the “singularity” problem existing in backstepping design procedure is avoided. Moreover, by using the finite-time stability criterion, it is proven that the proposed control strategy can ensure the boundedness of the whole system variables and achieve all the state tracking errors evolve within the predesigned performance regions in finite time. Finally, the effectiveness of the proposed control strategy is verified by using some simulation results.

This article studies the distributed observer-based event-triggered bipartite tracking control problem for stochastic nonlinear multiagent systems with input saturation. First, different from conventional observers, we construct a novel distributed reduced-order observer to estimate unknown states for the stochastic nonlinear systems. Then, an event-triggered mechanism with relative threshold is introduced to reduce the burden of communication. In addition, the bipartite tracking controller is proposed for stochastic multiagent systems by using fuzzy logic systems and the backstepping approach. Meanwhile, it is proved that the designed method can guarantee that all the signals in the closed-loop systems are bounded in probability, and the distributed consensus tracking errors can converge to a small neighborhood of the origin via the Lyapunov stability theory. Finally, a simulation example is given to prove the effectiveness of the designed scheme.

In this article, the singularity-free adaptive fuzzy fixed-time control problem is studied for an uncertain n-link robotic system with the position tracking error constraint. The controlled robotic system can be described as a multiple-input-multiple-output system.To implement the user-defined performance, an improved error conversion mechanism based on performance functions is presented such that the converted error is limited to an interval greater than zero, and an appropriate barrier Lyapunov function (BLF) is constructed to avoid the breach of position tracking error constraint. The fuzzy approximator is utilized to estimate the unknown functions. The significance and challenges of this article are to establish a new error conversion mechanism and design corresponding BLF that can be integrated into fixed-time control design to present a singularity-free adaptive fuzzy fixed-time control scheme. Benefits of the proposed adaptive fixed-time controller in comparison to the current approaches are that it cannot cause the singularity issue appearing in backstepping-based fixed-time control design and ensures quick transient response. Combining with Lyapunov stability theory, the boundedness of the closed-loop signals is ensured, and the position tracking error can be constrained in the user-defined performance boundaries. Finally, simulation results demonstrate the feasibility of the proposed control strategy.

This article investigates the event-triggered control problem for stochastic nonlinear systems with unmeasured states and unknown backlash-like hysteresis. Based on the fuzzy logic systems, the unknown nonlinear functions can be identified. Then, by utilizing a fuzzy state observer, the unmeasured states of the considered system can be estimated. Moreover, by introducing an event-triggered mechanism, the communication load can be largely reduced. By employing the backstepping control strategy and the adaptive control method, a novel adaptive fuzzy event-triggered control method is constructed. It is shown that whole signals in the closed-loop systems are, ultimately, semiglobally and uniformly bounded in probability. Moreover, the tracking errors and the observer errors are located in a small neighborhood around the origin. Finally, a numerical example is given to confirm the effectiveness of the design scheme.

This article considers the event-driven fault detection (FD) problem for discrete-time interval type-2 (IT2) fuzzy networked control systems (NCSs) with nonlinear perturbations. To reduce signal transmission, an event-driven mechanism in the NCSs is introduced. A novel FD fuzzy filter is designed for generating a residual signal and detecting actuator faults by considering the event-driven strategy, nonlinear perturbations, communication delay, and mismatched membership functions. It is shown that the proposed event-driven FD technique is effective to optimize performance compared with the existing FD strategy. Finally, two examples are presented to verify the advantages of the novel design technique.

This article focuses on the containment control problem for nonlinear multiagent systems (MASs) with unknown disturbance and prescribed performance in the presence of dead-zone output. The fuzzy-logic systems (FLSs) are used to approximate the unknown nonlinear function, and a nonlinear disturbance observer is used to estimate unknown external disturbances. Meanwhile, a new distributed containment control scheme is developed by utilizing the adaptive compensation technique without assumption of the boundary value of unknown disturbance. Furthermore, a Nussbaum function is utilized to cope with the unknown control coefficient, which is caused by the nonlinearity in the output mechanism. Moreover, a second-order tracking differentiator (TD) is introduced to avoid the repeated differentiation of the virtual controller. The outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders. It is shown that all the signals are semiglobally uniformly ultimately bounded (SGUUB), and the local neighborhood containment errors can converge into the prescribed boundary. Finally, the effectiveness of the approach proposed in this article is illustrated by simulation results.

This paper discusses adaptive event-triggered fault detection filter design for fuzzy stochastic models with missing measurements. First, a novel event-triggered strategy is introduced, while an adaptive law is provided to adjust communication threshold dynamically. Compared with traditional event-triggered methods with fixed threshold, the proposed strategy is more effective on saving network communication resources. Second, a Bernoulli stochastic process is proposed to describe the measurement missing phenomenon, which always appears in real network environment. Then, an integrated fault detection model for fuzzy stochastic systems is constructed by taking network-induced delays, adaptive event-triggered strategy and missing measurements into account. A new method is provided to achieve mean-square asymptotical stability of residual model with one desired fault detection objective. Finally, simulation cases are introduced to verify the validity of the designed strategy.

This paper investigates the problem of event-triggered fault detection (FD) filter design for nonlinear networked systems in the framework of interval type-2 fuzzy systems. In the system model, the parameter uncertainty is captured effectively by the membership functions (MFs) with upper and lower bounds. For reducing the utilization of limited communication bandwidth, an event-triggered communication mechanism is applied. A novel FD filter subject to event-triggered communication mechanism, data quantization, and communication delay is designed to generate a residual signal and detect system faults, where the premise variables are different from those of the system model. Consequently, the augmented FD system is with imperfectly matched MFs, which hampers the stability analysis and FD. To relax the stability analysis and achieve a better FD performance, the information of MFs and slack matrices are utilized in the stability analysis. Finally, two examples are employed to demonstrate the effectiveness of the proposed scheme.

This paper investigates the dynamic output feedback control problem for interval type-2 (IT2) fuzzy systems. A switched output feedback controller, which depends on the values of membership functions, is constructed. The membership functions of IT2 fuzzy systems contain parameter uncertainties and are different from the type-1 Takagi-Sugeno fuzzy systems. Based on the IT2 fuzzy set theory, the parameter uncertainties can be effectively obtained by upper and lower membership functions. A novel IT2 switched output feedback controller is designed to ensure that the closed-loop system is asymptotically stable with an H∞ performance. Finally, a mass-spring-damping system is proposed to show the feasibility and the merit of the proposed scheme over the other existing ones.

This paper investigates the problem of filter design for interval type-2 (IT2) fuzzy systems with D stability constraints based on a new performance index. Attention is focused on solving the H∞, L2-L∞, passive, and dissipativity fuzzy filter design problems for IT2 fuzzy systems with D stability constraints in a unified frame. Under the new performance index frame, using Lyapunov stability theory, a novel type of IT2 filter is designed such that the filtering error system guarantees the prescribed H∞, L2-L∞, passive, and dissipativity performance levels with D stability constraints. The existence condition of the IT2 filter is expressed as the convex optimization problem, and the filter parameters in the condition can be solved by the standard software. The IT2 fuzzy model and IT2 fuzzy filter do not need to share the same lower and upper membership functions. Finally, a numerical example is provided to show the effectiveness of the proposed results.