This work is concerned with the finite‐time consensus of leader‐following non‐linear multi‐agent systems by means of distributed event‐triggered impulsive control. The finite‐time consensus protocol is first put forward based on event‐triggered impulsive strategies, where the impulsive instants are determined by the proposed event‐triggered condition. For the event‐triggered condition, it not only determines the impulsive instants but also effects the update time of the finite‐time control. Moreover, compared with the existing finite‐time controllers, the controller designed in this work does not contain any sign function, thereby overcoming the chattering phenomenon. In addition, without finding Zeno behaviour, the feasibility of the proposed control protocol is demonstrated. Lastly, simulations are employed to demonstrate the effectiveness of the proposed control schemes.

Scattering and turbulence are the main factors affecting imaging in natural water environment. A new model for underwater turbulent degradation is obtained by calculating the scattering transfer factors including the intensity distribution of beam propagation, turbulent fluid medium and suspended particle. In order to verify the proposed model, a controlled laboratory simulation system of turbulent water is established, from which the degradation transfer factor is measured and compared by image restoration and reconstruction. The new model is also applied for field tests in natural ocean water. Experimental results show that based on the proposed model, effect of image restoration and reconstruction can be substantially improved, which proves the correctness and validity of the model.

In this paper, we investigate the finite-time synchronization problem for a class of Markovian jumping complex networks (MJCNs) with non-identical nodes and impulsive effects. Sufficient conditions for the MJCNs are presented based on an M-matrix technique, Lyapunov function method, stochastic analysis technique, and suitable comparison systems to guarantee finite-time synchronization. At last, numerical examples are exploited to illustrate our theoretical results, and they testify the effectiveness of our results for complex dynamic systems.

This paper mainly tends to investigate finite-time stability and stabilization of impulsive stochastic delayed neural networks with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs). Firstly, by constructing the proper Lyapunov-Krasovskii functional and employing the average impulsive interval method, several novel criteria for ensuring the finite-time stability of impulsive stochastic delayed neural networks are obtained by means of linear matrix inequalities (LMIs). Then, some conditions about the state feedback controller are derived to ensure the finite-time stabilization of impulsive stochastic delayed neural networks with ROUs and RONs. Finally, numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed results.