This paper intends to propose a fully distributed consensus protocol under the dual-channel dynamic event-triggered strategies. First, an observer is devised to estimate the unmeasurable system state, and a dual-channel triggering strategies are established to save the network resources. Further, a fully distributed control protocol is devised to achieve consensus control without requiring the global topology information. The major superiority of the proposed method is that the controller, the observer, and their respective triggering conditions can be designed separately without affecting each other. Finally, simulations are presented for illustrating the validity of the theoretical result.

This article proposes a new separation design method for the well-known event-triggered output feedback control for networked control systems (NCSs). Considering a scenario in which the sensor and controller transmit their data via the dual-channel communication network, two independent dynamic event-triggered strategies are well-designed to reduce the consumption of network resources. Furthermore, a novel separation design method for the event-triggered output feedback control is presented, in which the controller, the observer, and their respective triggering conditions can be designed separately. Compared with the existing separation design methods, the main advantage of the proposed method is that no constraint on the dual-channel triggering thresholds is required. The simulation results exemplify the merits of the theoretical results with a comparison.

This article designs a new hierarchical distributed data-driven adaptive learning control algorithm to accomplish the leader-following tracking control objective for nonaffine nonlinear multiagent systems (MASs). The proposed hierarchical control structure is composed of a distributed observer and a decentralized data-driven adaptive learning controller. Considering that some followers cannot directly receive information from the leader, a distributed observer is designed to estimate the information of the leader. Based on this, a decentralized data-driven adaptive learning controller is further devised to enable the follower to track the estimated information of the leader, where the model parameter learning algorithm is developed to capture the dynamic characteristics of the original system. One advantage of the developed hierarchical control learning algorithm is that neither the leader's system model nor the follower's system model is needed. The other one is the elimination of the noncausal problem without the additional assumption. Simulation results exemplify the merits of the theoretical results by comparisons.

This article investigates the distributed control problem for nonlinear multiagent systems (MASs) with unknown system models. A novel distributed model-free adaptive learning algorithm is developed to learn a controller from the online system data. Notably, a significant advancement over conventional methods is that the proposed algorithm requires only local interaction data from neighboring agents, eliminating dependencies on both a priori system structural knowledge and global topology information. Comprehensive simulations validate the theoretical results and demonstrate the superior efficacy of the devised algorithm.

This article studies the data-driven distributed vehicle platoon control problem for heterogeneous nonlinear vehicle systems. The heterogeneous nonlinear vehicle systems are first transformed into the linear data model with the increment form. Then, a novel data-driven distributed cooperative controller is devised to accomplish the vehicle platoon control objective, where the stability analysis problem is converted into the feasibility problem with the help of the linear matrix inequality technique. The main advantage of the devised control algorithm is that it only utilizes the position and speed information of neighboring vehicles and does not require any pre-training process. Finally, simulation is given to demonstrate the devised control algorithm with comparisons.

The problem of the model-free adaptive resilient control (MFARC) for nonlinear cyber-physical systems (CPSs) suffered from aperiodic jamming attacks is investigated in this article. First, the MFARC framework subject to aperiodic jamming attacks is established, and an intermediate variable method is introduced to avoid using the unavailable time-varying parameter and further eliminate an extra assumption on the sign limit of it. Then, a MFARC scheme is devised to track the desired output, where the problem of the tracking control can be transformed into solving a feasibility problem, and the controller parameters can be obtained with the aid of the linear matrix inequality technique. What is more, a novel attack compensation mechanism is developed in the MFARC scheme to mitigate the impact of aperiodic jamming attacks. In the last, an example is provided to verify the effectiveness of the devised MFARC scheme.

This article addresses the distributed model-free adaptive control (DMFAC) problem for learning nonlinear multiagent systems (MASs) subjected to denial-of-service (DoS) attacks. An improved dynamic linearization method is proposed to obtain an equivalent linear data model for learning systems. To alleviate the influence of DoS attacks, an attack compensation mechanism is developed. Based on the equivalent linear data model and the attack compensation mechanism, a novel learning-based DMFAC algorithm is developed to resist DoS attacks, which provides a unified framework to solve the leaderless consensus control, the leader-following consensus control, and the containment control problems. Finally, simulation examples are shown to illustrate the effectiveness of the developed DMFAC algorithm.

The problem of the observer-based fully distributed containment control for multiagent systems (MASs) subject to denial-of-service (DoS) attacks is investigated in this article. First, a switched fully distributed control framework is established for a class of DoS attacks constrained by the attack duration. Then, a novel attack-resilient control scheme is developed to accomplish the containment control task. The major advantages of the devised control scheme are that any information of the whole network topology structure is not involved and only the information from neighbor agents is used. What is more, a novel observer-based attack compensator is devised to resist DoS attacks. Finally, a practical example of the mobile robot system is presented to testify the validity of the designed control scheme by a comparison.

This article studies the observer-based event-triggered containment control problem for linear multiagent systems (MASs) under denial-of-service (DoS) attacks. In order to deal with situations where MASs states are unmeasurable, an improved separation method-based observer design method with less conservativeness is proposed to estimate MASs states. To save communication resources and achieve the containment control objective, a novel observer-based event-triggered containment controller design method based on observer states is proposed for MASs under the influence of DoS attacks, which can make the MASs resilient to DoS attacks. In addition, the Zeno behavior can be eliminated effectively by introducing a positive constant into the designed event-triggered mechanism. Finally, a practical example is presented to illustrate the effectiveness of the designed observer and the event-triggered containment controller.

This paper investigates the state estimation problem for the DC microgrids with constant power loads in the case of denial-of-service attacks. By using the centralized control schemes, the DC microgrids could send measurements to the control center through a transmission network. In addition, according to the nonlinear and cyber attacks nature of the DC microgrids with multiple constant power loads, a Cubature Kalman filter is designed to estimate the DC microgrids state and be adapted to the packet losses caused by cyber attacks. Finally, simulation studies are given to illustrate the effectiveness of the designed filter.