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 article, we consider controlling a class of single-input–single-output (SISO) commensurate fractional-order nonlinear systems with parametric uncertainty and external disturbance. Based on the backstepping approach, an adaptive controller is proposed with adaptive laws that are used to estimate the unknown system parameters and the bound of unknown disturbance. Instead of using discontinuous functions, such as the sign function, an auxiliary function is employed to obtain a smooth control input that is still able to achieve perfect tracking in the presence of bounded disturbances. Indeed, the global boundedness of all closed-loop signals and asymptotic perfect tracking of fractional-order system output to a given reference trajectory are proved by using fractional directed Lyapunov method. To verify the effectiveness of the proposed control method, simulation examples are presented.

In this article, we investigate the distributed resilient observers-based decentralized adaptive control problem for cyber-physical systems (CPSs) with time-varying reference trajectory under denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear multi-input uncertain multiagent systems, which can be used to model an AC microgrid system consisting of distributed generators. When the communication to a subsystem from one of its neighbors is attacked by a DoS attack, the transmitted information is unavailable and the existing distributed adaptive methods used to estimate the bound of the nth-order derivative of the reference trajectory become nonapplicable. To overcome this difficulty, we first design a new distributed estimator for each subsystem to ensure that the magnitude of the state of the estimator is larger than the bound of the nth-order derivative of the reference trajectory after a finite time. By employing the estimator state, a distributed observer with a switching mechanism is proposed. Then, a new block backstepping-based decentralized adaptive controller is developed. Based on the DoS communication duration property, convex design conditions of observer parameters are derived with the Lebesgue integral theory and the average dwell time method. It is proved that the output tracking errors will approach a compact set with the developed method. Finally, the design method is successfully applied to show the effectiveness of the proposed method to solve the power sharing problem for AC microgrids.

In the paper, we propose a novel event-triggered tracking framework for fast and robust visual tracking in the presence of model drift and occlusion. The resulting tracker not only operates at real-time, but also is resilient to tracking failures caused by factors such as heavy occlusion. Specifically, the tracker consists of an event-triggered decision model as the core module that coordinates other functional modules, including a short-term tracker, occlusion and drift identification, target re-detection, short-term tracker updating and on-line discriminative learning for detector. Each functional module is associated with a defined event that is triggered when a set of proposed conditions are met. The occlusion and drift identification module is intended to perform on-line evaluation of the short-term tracking. When a model drift event occurs, the target re-detection module is activated by the event-triggered decision model to relocate the target and reinitialize the short-term tracker. The short-term tracker updating is carried out at each frame with a variable learning rate depending on the degree of occlusion. A sampling-pool is constructed to store discriminative samples that are used to update the detector model. Extensive experiments on large benchmark datasets demonstrate that ETT can effectively detect model drift and restore tracking.