This paper is concerned with cyber attack detection problem in a platoon-based vehicular networked control system. In such a system, the information among vehicles is transmitted through a shared wireless communication network and also each vehicle has access to its own information measured by local sensors. These kind of systems are highly vulnerable to cyber attacks and therefore, cyber-security issues need to be properly addressed to ensure the safety of the systems. Among various cyber-security aspects, reliable attack detection is of utmost importance as the ability to detect cyber attacks in a timely manner can reduce the damage to the systems. Therefore, we present a cyber attack detection algorithm that is capable of detecting attacks violating both measurements and control command data. This algorithm is based on an ellipsoidal set-membership filtering approach which consists of two sets: prediction ellipsoid set and an estimation ellipsoid set calculated through updating the prediction ellipsoid set with the measurement data. The detection method depends on the existence of intersection between these two sets computed by the filter. Simulation results for some possible cyber attacks are provided to demonstrate the effectiveness of the proposed method.Index Terms-Cyber attack detection, platoon-based vehicles, Set-membership filtering, networked control systems.
This article addresses the grid-connected single-phase photovoltaic (PV) inverter control. A long-horizon finite-set model predictive control (MPC) strategy is proposed to control the voltage source inverter. To achieve this, a multi-step implementation approach and a control sequence rearrangement method are designed to reduce the sampling frequency and switching frequency. The optimization problem for the finite-set MPC is further simplified to reduce the computational complexity of the optimization procedure. Moreover, a multi-step delay compensation method is developed to compensate for the computational delay of the control algorithm. Finally, the proposed control method is implemented in a grid-connected PV inverter and simulation test results demonstrate its effectiveness under different load and generation conditions.
This paper addresses the cyber-security issue of microgrid energy management system, where cyberattacks, appearing in communication networks, corrupt the transmitted data and falsify the state estimates. This can potentially threaten the physical system and lead to severe physical consequences. Therefore, it is of great significance to detect, locate and tolerate cyber-attacks. To this end, set-membership estimation is employed to detect the occurrence and locate the position of DoS attacks. The model predictive control technique is utilised to schedule the energy management by using the forecasts of photovoltaic generation and load demand. It is shown that the cyber-attack localisation and the desired tolerant control performance against attacks can be both achieved. Simulation results are provided to demonstrate the effectiveness of the proposed strategy.
This paper proposes a real-time monitoring framework for a landslide susceptibility area based on wireless sensor network using multiple Unmanned Aerial Vehicles (UAVs). Many researchers have considered building a landslide susceptibility map to distinguish different levels of landslide susceptible zones. However, to prevent damage from landslides, it is more important for the disaster control center to identify the time and location of the landslide occurrence in those highly susceptible areas. Hence, a rain-triggered landslide monitoring system is proposed herein for local mountain areas. First, a wireless sensor network framework is constructed to inform the control center as immediately as possible when landslides occur. Second, multiple UAV sensors will be responsible for collecting the stereo images of the slope in highly sensitive zones on schedule. Based on the stereo images and the binocular model, in-depth information can be obtained. With the depth information and Speeded Up Robust Features (SURF) detection, the key point characteristic information is constructed as the input data for Support Vector Machine (SVM). An SVM algorithm is designed with Python program language and executed in real time. Using this algorithm, the real-time images collected by UAVs and the landslide warning information will be sent to the control center for further analysis. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method.
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