This paper is concerned with the issue of platoon fault-tolerant control of the system with time-varying actuator faults. By obtaining the relative state information of neighboring vehicles, two event-triggered fault-tolerant controllers are designed for the two cases of the leader vehicle speed. Then the vehicle platoon system is transformed into error system, and the event-triggered control strategy is designed, in an effort to further save resources. Moreover, by the related theory of Lyapunov, it is shown that the error system is bounded. Finally, two simulation examples are given to show the effectiveness of the proposed approach.

This paper focuses on the consensus problem of a vehicle platoon system with time-varying topology via self-triggered control. Unlike traditional control methods, a more secure event-triggered controller considering the safe distance was designed for the vehicle platoon system. Then, a Lyapunov function was designed to prove the stability of the platoon system. Furthermore, based on the new event-triggered function, a more energy efficient self-triggered control strategy was designed by using the Taylor formula. The new self-triggered control strategy can directly calculate the next trigger according to the state information of the last trigger. It avoids continuous calculation and measurement of vehicles. Finally, the effectiveness of the proposed two self-triggered control strategies were verified by numerical simulation experiments.

Industrial control network is a direct interface between information system and physical control process. Due to the lack of authentication, encryption, and other necessary security protection designs, it has become the main target of malicious attacks under the trend of increasing openness. In order to protect the industrial control systems, we examine the detection of abnormal traffic in industrial control network and propose a method of detecting abnormal traffic in industrial control network based on autoencoder technology. What is more, a new deep autoencoder model was designed to reduce the dimensionality of traffic data in industrial control network. In this article, the Kullback–Leibler divergence was added to the loss function to improve the ability of feature extraction and the ability to recover raw data. Finally, this model was compared with the traditional data dimensionality reduction method (principal component analysis (PCA), independent component analysis, and singular value decomposition) on gas pipeline dataset. The results show that the approach designed in this article outperforms the three methods in different scenes in terms of f₁ score.

This paper is concerned with the joint state and input estimation problem for linear discrete time-varying systems over peer-to-peer sensor networks, in which deception attacks are taken into account. By resorting to singular value decomposition, a local estimator structure is proposed to jointly estimate the system states and unknown inputs. Then, a distributed state estimator is constructed by fusing local estimates and covariance matrices. In addition, a sufficient condition is provided to ensure the uniformly bounded estimation error (in mean square) in each node. Finally, a numerical example is provided to show the effectiveness of the proposed estimation algorithm.