The distributed tracking problem for uncertain nonlinear multiagent systems (MASs) under event-triggered communication is an important issue. However, existing results provide solutions that can only ensure stability with bounded tracking errors, as asymptotic tracking is difficult to be achieved mainly due to the errors caused by event-triggering mechanisms and system uncertainties. In this article, with the aim of overcoming such difficulty, we propose a new methodology. The subsystems in MASs are divided into two groups, in which the first group consists of the subsystems that can access partial output of the reference system and the second one contains all the remaining subsystems. To estimate the state of the reference system, a new distributed event-triggered observer is first designed for the first group based on a combined output observable condition. Then, a distributed event-triggered observer is proposed for the second group by employing the observer state of the first group. Based on the designed observers, adaptive controllers are derived for all subsystems. It is established that global stability of the closed loop system is ensured and asymptotic convergence of all the tracking errors is achieved. Moreover, a simulation example is provided to show the effectiveness of the proposed method.

In this note, the event-triggered adaptive control for a class of uncertain nonlinear systems is considered. Unlike the traditional adaptive event-triggered control, the controller and parameter estimator are event-triggered simultaneously. Asymptotical convergence of stabilization error is guaranteed. To solve this problem, we design a set of event-triggering conditions which is updated for each triggering. At the same time the ISS assumption is not needed. It is shown that the proposed control schemes guarantee that all the closed-loop signals are globally bounded and the stabilization error converges to the origin asymptotically. Simulation results illustrate the effectiveness of our scheme.

In this article, the adaptive control for a class of strict-feedback nonlinear systems with uncertainties under injection and deception attacks is considered. An adaptive control scheme is proposed to deal with the injection and deception attacks meanwhile guarantee that regulation errors could be made arbitrarily small by adjusting control parameters. Compared with existing works whose models are linear or relatively simple, the model we consider in this article is nonlinear with parametric uncertainties. A new type of feedback control scheme is introduced to solve this problem. A simulation example is given to verify the effectiveness of our proposed control scheme.

For antiswing control of underactuated cranes, how to guarantee the converging speed of cranes through control design is essential but still remains unsolved. In this article, the adaptive antiswing control for underactuated gantry cranes with guaranteed transient performance under unmodeled dynamics and external disturbances is investigated. To sovle this problem, a set of filters are proposed to make the backstepping technique applicable for the control of crane systems. Then through variable transformation the position error and swing angel could be guaranteed converging to the origin with a given exponential speed. Hardware experiments are conducted to show that the proposed scheme achieves better control performance over existing methods, and it is illustrated that the proposed control scheme possesses strong robustness to unmodeled uncertainties and external disturbances.

This article investigates the leaderless consensus problem for uncertain high-order nonlinear multiagent systems with event-triggered communication. Under a directed graph condition, a fully distributed adaptive control strategy is presented. The main contributions are summarized as follows: 1) globally Lipschitz condition, as required in many existing literatures, is not needed in this article; 2) for each agent, only one filtered output signal is required to be broadcast to its neighbors, which can effectively relieve network transmission burden; 3) the distributed adaptive controllers and event-triggered conditions are co-designed based on a single Lyapunov function such that continuous monitoring of neighbors’ states is not needed; and 4) by introducing an exponential convergence term to the designed triggering condition, Zeno behavior is avoided while all the agents’ outputs can reach asymptotically leaderless consensus. Moreover, to easily implement the designed triggering condition and further reduce the triggering frequency when the consensus errors converge to the neighborhood of origin, a switching type of triggering condition is presented and bounded consensus can be guaranteed. Simulation results are given to validate the presented distributed consensus control scheme.

This paper investigated the dynamic event-triggered control scheme for the path-following activity of underactuated surface vehicle (USV) subject to actuator saturation and gain uncertainties. Specifically, a dynamic event-triggered mechanism with the adjustable threshold is developed to reduce the communication burden and the execution wear of actuators. Unlike most existing event-triggered results, in which the threshold parameters are always fixed, the developed mechanism can ensure that the triggering thresholds are updated online in an adaptive manner, so as to achieve better resource efficiency. Furthermore, taking the practical measurable variables (i.e., revolving speed of propeller and rudder angle) as the control input, an auxiliary system is designed to deal with the influence of actuator saturation, which is distinct from most existing saturation-tolerant methods for USV. For merits of the neural damping technique, both the internal and external uncertainties are remodeled and only four adaptive parameters require to be updated online. Considerable effort has been made to guarantee the semi-globally uniform ultimate bounded (SGUUB) stability. Finally, the numerical simulation and physical experiment are illustrated to demonstrate the remarkable performance of the proposed algorithm.