Path Tracking Control of Autonomous Vehicles Subject to Deception Attacks via a Learning-Based Event-Triggered Mechanism

Extended state observer-based backstepping control for nonlinear systems under FDI attacks

In this paper, a novel extended state observer-based backstepping control scheme is proposed for strict-feedback nonlinear systems with unmeasured states suffering from false data injection (FDI) attacks. An extended state observer is designed to achieve simultaneous online estimation of system states and FDI attacks. A secure output feedback tracking control scheme with an attack compensation method is proposed to reduce the influence of FDI attacks. It is proven that the proposed scheme can guarantee that the closed-loop system is semi-global uniformly ultimately bounded. Moreover, it is shown that the observation errors can be as small as desired with an adjustable parameter and the tracking error can converge to a small neighborhood of the origin. Finally, two simulation examples verify the efficiency of the proposed approach.

A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems

The automated guided vehicle (AGV) dispatching problem is to develop a rule to assign transportation tasks to certain vehicles. This article proposes a new deep reinforcement learning approach with a self-attention mechanism to dynamically dispatch the tasks to AGV. The AGV dispatching system is modeled as a less complicated Markov decision process (MDP) using vehicle-initiated rules to dispatch a workcenter to an idle AGV. In order to deal with the highly dynamical environment, the self-attention mechanism is introduced to calculate the importance of different information. The invalid action masking technique is performed to alleviate false actions. A multimodal structure is employed to mix the features of various sources. Comparative experiments are performed to show the effectiveness of the proposed method. The properties of the learned policies are also investigated under different environment settings. It is discovered that the policies explore and learn the properties of different systems, and also smooth the traffic congestion. Under certain environment settings, the policy converges to a heuristic rule that assigns the idle AGV to the workcenter with the shortest queue length, which shows the adaptiveness of the proposed method.

Resilient event-triggering adaptive neural network control for networked systems under mixed cyber attacks

This paper addresses the resilient event-triggering adaptive neural network (NN) control problem for networked control systems under mixed cyber attacks. Compared with the conventional event-triggered mechanism (ETM) with constant threshold, a novel resilient ETM is designed to withstand the affect of denial-of-service attacks and conserve communication resources. Different from the energy-bounded deception attacks, an unknown state-dependent nonlinear attack signal is considered in this work. To identify the deception attack, the NN technique is utilized to approximate the unknown attack signal. Subsequently, an adaptive controller is established to compensate for the malicious affects of deception attacks on the system. Furthermore, sufficient conditions for the boundedness of the system are derived via applying the Lyapunov functional, and a co-design strategy for control gain and event-triggering parameter is provided. Finally, the feasibility of the proposed approach is validated through a robot manipulator system.

Event-Based Intermittent Formation Control of Multi-UAV Systems Under Deception Attacks

This article investigates the problem of event-based intermittent formation control for multi-UAV systems subject to deception attacks. Compared to the available research studies on multi-UAV systems with continuous control strategy, the proposed intermittent control strategy saves a large amount of computation resources. An average method is introduced in developing the event-triggered mechanism (ETM) such that the amount of unexpected triggering events induced by uncertain disturbances is greatly reduced. Moreover, such a mechanism can further decrease the average data-releasing rate, thereby alleviating the burden of network bandwidth. Sufficient conditions for multi-UAV systems with deception attacks to achieve the predefined formation are obtained with the aid of Lyapunov stability theory. Finally, the validity of the proposed theoretical results is demonstrated via a simulation example.

Memory-event-triggered consensus control for multi-UAV systems against deception attacks

This paper addresses the memory-based event-triggered consensus control issue for multi-UAV systems subject to deception attacks. In order to alleviate network bandwidth burden and reduce unnecessary data transmission, a memory-based event-triggered scheme (METS) is proposed by applying historic data information (HDI). Meanwhile, the average mechanism (AM) is introduced to replace the input of conventional event triggering scheme, which eliminates adverse event-triggering caused by instantaneous random jitter and deception attacks. Through this method, data mutation, peak/trough information loss, and energy consumption issues of UAV can be effectively addressed. With the aid of attack observer, a consensus control strategy is devised for each UAV to achieve control objective and compensate for the impact of attacks on multi-UAV system. Then, sufficient conditions are constructed to co-design the parameters and guarantee the attacked multi-UAV system can achieve consensus. The simulation results are provided to demonstrate the validity and practicality of the proposed strategy.

Event-based formation control for multiple unmanned aerial vehicles under directed topology

This paper focuses on the problem of formation control for multiple unmanned aerial vehicles (UAV) subject to cyber attacks by a novel event-triggered communication scheme. An average method is introduced to design the triggering condition of this communication scheme, by which the amount of wrong triggering events caused by the sudden change of system states is greatly decreased, thereby saving a great deal of network bandwidth and reducing network congestion. Considering cyber attacks, a new event-based formation control strategy is developed for multi-UAV systems under directed topology by utilizing a control compensation approach. Sufficient conditions for the multi-UAV system to achieve the desired formation are acquired. Finally, a simulation example is undertaken to demonstrate the effectiveness of the theoretical results.

Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology

Artificial intelligence (AI) technology is crucial for developing autonomous ships in the maritime industry. Autonomous ships, based on the collected information, recognize the environment without any human intervention and operate themselves using their own judgment. However, ship-to-land connectivity increased, owing to the real-time monitoring and remote control (for unexpected circumstances) from land; this poses a potential cyberthreat to various data collected inside and outside the ships and to the applied AI technology. For the safety of autonomous ships, cybersecurity around AI technology needs to be considered, in addition to the cybersecurity of the ship systems. By identifying various vulnerabilities and via research cases of the ship systems and AI technologies, this study presents possible cyberattack scenarios on the AI technologies applied to autonomous ships. Based on these attack scenarios, cyberthreats and cybersecurity requirements are formulated for autonomous ships by employing the security quality requirements engineering (SQUARE) methodology.

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance

In recent years, the unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the planning, design and maintenance of urban distributed photovoltaic arrays (UDPA). However, the existing studies rarely concern the UAV swarm scheduling problem when applied to remoting sensing in UDPA maintenance. In this study, a novel scheduling model and algorithm for UAV swarm remote sensing in UDPA maintenance are developed. Firstly, the UAV swarm scheduling tasks in UDPA maintenance are described as a large-scale global optimization (LSGO) problem, in which the constraints are defined as penalty functions. Secondly, an adaptive multiple variable-grouping optimization strategy including adaptive random grouping, UAV grouping and task grouping is developed. Finally, a novel evolutionary algorithm, namely cooperatively coevolving particle swarm optimization with adaptive multiple variable-grouping and context vector crossover/mutation strategies (CCPSO_-mg-cvcm), is developed in order to effectively optimize the aforementioned UAV swarm scheduling model. The results of the case study show that the developed CCPSO_-mg-cvcm significantly outperforms the existing algorithms, and the UAV swarm remote sensing in large-scale UDPA maintenance can be optimally scheduled by the developed methodology.