In this paper, an output feedback adaptive fuzzy leader-following event-triggered consensus (ETC) problem for switched nonlinear multi-agent systems (SNMASs) is investigated. In the considered systems, only outputs of leader and followers are measurable at sampling instants. To deal with this problem, a novel auxiliary system and observers are employed. Then, corresponding error systems are established and the consensus problem is transformed into a stabilization problem. To achieve better approximation ability for nonlinear uncertainties, the interval type-2 fuzzy logic systems (IT2FLSs) are utilized. In order to reduce the wastage of communication resources (WCRs), an adaptive fuzzy ETC protocol with a discrete-time switching threshold event-triggering mechanism (ETM) which is monitored only at sampling instants is provided. It is proved that the output feedback adaptive fuzzy ETC protocol enables the realization of the consensus target. Finally, to verify the effectiveness of the proposed scheme, the ETC protocol is applied to a practical example.

This article presents a discussion on the adaptive fuzzy fully distributed consensus problem of heterogeneous switched nonlinear multiagent systems (SNMASs). The considered agents contain both first- and second-order dynamics. Fuzzy logic systems are introduced to approximate the unknown nonlinear terms of the SNMASs. Influenced by the systems' own factors or the external environment, the subsystems of agents may be unstabilizable and existing control strategies may not be able to ensure the stability of the systems. Therefore, it is necessary to provide a new consensus protocol to address this problem. In this article, a fully distributed consensus protocol is provided that includes an auxiliary system and a series of state-dependent switching laws. To ensure the stability of whole SNMASs and realize the consensus objective, convex combination technology and the single Lyapunov function method are adopted. Finally, the effectiveness of the proposed scheme is verified through simulation results.

This article studies sampled-data stabilization for a class of fractional-order switched nonlinear systems (FOSNSs) with arbitrary switching. The feasibility of using a fuzzy-logic system (FLS) to approximate the fractional-order systems (FOSs) is proved. Using backstepping method, the fractional-order adaptive update laws and sampled-data controller for the discussed FOSNSs are designed based on the FLS. Under the proposed sampled-data control scheme, it is proved that solutions of the studied FOSNSs are semi-globally uniformly ultimately bounded (SGUUB). Two examples are given to verify the effectiveness of the proposed sampled-data control scheme.

In this study, an adaptive fuzzy decentralized dynamic surface control (DSC) problem is investigated for switched large-scale nonlinear systems with deferred asymmetric and time-varying full-state constraints. Due to the existence of additional general nonlinearities, complicated output interconnections, and full-state constraints, it is difficult to address the above control problem using existing methods. Fuzzy-logic systems are, therefore, utilized to approximate the unknown nonlinear functions, and the DSC technique is adopted to overcome the "curse of dimensionality" problem. A novel fuzzy adaptive decentralized controller design is presented using the proposed convex combination technique. Furthermore, it is proven that under the proposed controller and state-dependent switching law, all states of the closed-loop system are bounded and deferred asymmetric, and the time-varying full-state constraints are strictly obeyed. The simulation results are presented to demonstrate the effectiveness of the proposed method.

A decentralized adaptive fuzzy sampled-data control problem for switched large-scale nonlinear systems with time-varying delays is considered in this article. Fuzzy logic systems are applied to handle unknown nonlinear terms. A novel fuzzy adaptive law is proposed utilizing only the information of the system states at sampling instants. Moreover, a proper CLF and a new decentralized adaptive sampled-data control law are constructed to ensure that all states of the CLS are bounded. The developed strategy’s effectiveness is verified with two examples.

This article focuses on an adaptive fuzzy dynamic event-triggered tracking control for nonlinear time-delay systems with unmodeled dynamics via a command filter method. Fuzzy logic systems are utilized to address the unknown nonlinear functions. The upper bound of the approximation error is allowed to be unknown and can be compensated by skillfully introducing a hyperbolic tangent function to the design of the adaptive laws. Meanwhile, without requiring any assumptions, time delays can be handled by appropriately incorporating the delayed nonlinear functions into the Lyapunov–Krasovskii functional. Then, a dynamic event-triggered control mechanism is designed to dynamically adjust the threshold parameter. Finally, a new adaptive controller is constructed such that all states of the closed-loop system are bounded. The system output is demonstrated to follow the desired signal. Two examples are given to illustrate the validity of the presented method.

This article presents a decentralized fuzzy adaptive event-triggered command-filtered control scheme for switched large-scale nonlinear systems with input delay. Fuzzy logic systems are employed to approximate uncertain nonlinearities and a novel observer is constructed to estimate unmeasured states. The “explosion of complexity” defect inherent in the backstepping approach is overcome by employing command filter technology. An auxiliary system is designed to compensate for the effect of input delay, and a novel event-triggered decentralized controller is derived based on the common Lyapunov function method. This article shows that the presented strategy guarantees that all closed-loop variables are semiglobally uniformly ultimately bounded. Finally, simulation results are shown to further confirm the presented strategy’s validity.

This study considers an adaptive neural-network (NN) periodic event-triggered control (PETC) problem for switched nonlinear systems (SNSs). In the system, only the system output is available at sampling instants. A novel adaptive law and a state observer are constructed by using only the sampled system output. A new output-feedback adaptive NN PETC strategy is developed to reduce the usage of communication resources; it includes a controller that only uses event-sampling information and an event-triggering mechanism (ETM) that is only intermittently monitored at sampling instants. The proposed adaptive NN PETC strategy does not need restrictions on nonlinear functions reported in some previous studies. It is proven that all states of the closed-loop system (CLS) are semiglobally uniformly ultimately bounded (SGUUB) under arbitrary switchings by choosing an allowable sampling period. Finally, the proposed scheme is applied to a continuous stirred tank reactor (CSTR) system and a numerical example to verify its effectiveness.

The adaptive fuzzy finite-time tracking control problem of a class of switched nonlinear systems is investigated in this study. Fuzzy logic systems are introduced to handle the unknown nonlinear terms in the considered system. To overcome the drawback in the recursive design method, a finite-time command filter is employed. By constructing a new state-dependent switching law and adaptive fuzzy control signal, the existing restrictions on subsystems of switched systems are relaxed, all subsystems of the considered system are allowed to be unstabilizable. To avoid the Zeno behavior, a new hysteresis switching law is derived. It is proven that all states of the closed-loop system are bounded in finite time under the proposed fuzzy finite-time control scheme. Additionally, the proposed control method is extended to a class of more general switched large-scale nonlinear systems. Finally, two examples are provided to verify the developed method's effectiveness.

The global output feedback stabilization problem is investigated in this paper via sampled-data control for switched nonlinear systems in the p-normal form. First, a reduced-order state observer is designed. Then, an output feedback sampled-data controller is constructed with the relaxation of some restrictions of switched nonlinear systems. The proposed controller can ensure that all states of the corresponding closed-loop system can converge to the origin. Simulation results are given to show the effectiveness of the proposed scheme.

This article investigates the sampled-data stabilization problem of a class of switched nonlinear systems. All subsystems of the considered system are allowed to be unstabilizable. To relax the restrictions on unknown nonlinear functions in some existing results, we use the nonlinear approximation ability of radial basis function neural networks. Novel mode-dependent adaptive laws and sampled-data control laws are constructed by only using the system states' information at sampling instants. A novel sampled-data switching condition is derived, which can avoid Zeno behavior effectively. To guarantee that all states of the closed-loop system (CLS) are bounded, a new allowable sampling period is deduced. Finally, we demonstrate the proposed method's effectiveness through two examples.

This paper considers the problem of sampled-data adaptive output feedback fuzzy stabilization for switched uncertain nonlinear systems associated with asynchronous switching. A state observer is designed to estimate the unmeasured states and fuzzy logic systems are employed to deal with the unknown nonlinear terms. Sampled-data controller and novel switched adaptive laws are constructed based on the recursive design method and an average dwell time constraint is given to ensure that the closed-loop system is stable. The proposed scheme is employed in a mass-spring-damper system to demonstrate its effectiveness.

This paper considers the global stabilization problem via sampled-data control for a class of switched nonlinear systems meanwhile taking into account asynchronous switching. First of all, a state feedback sampled-data controller is constructed by backstepping design method. Then, a relationship between the sampling period and the average dwell time is derived, which can guarantee that the closed-loop system is globally asymptotically stable. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed method.