Sensor-Fusion-Based Event-Triggered Following Control for Nonlinear Autonomous Vehicles Under Sensor Attacks

The situation of interest is where a vehicle is equipped with multiple sensors to measure the distance to the leading vehicle but does not need to obtain data from speed and acceleration sensors. The distance measurements are susceptible to asynchronous sampling and noise, nearly half of which may be manipulated by malicious attackers. In this situation, the event-triggered vehicle-following control problem of nonlinear autonomous vehicles with unknown parameters is studied. First, a secure event-triggered mechanism that can resist manipulation is devised to alleviate the burden of data transmission and processing caused by multiple sensors. Then, a novel adaptive sensor fusion algorithm is developed to estimate the actual distance. Subsequently, an improved adaptive observer is designed based on the event-triggered estimated distance to estimate continuous-time distance, velocity, acceleration, and system parameters. Finally, the following controller is designed using the estimated states and parameters with the help of Levant differentiators. The effectiveness of the proposed control scheme is validated through simulation studies.Note to Practitioners—This work aims to develop a secure following control method for nonlinear automated vehicles with unknown states and parameters, which can effectively handle sparse sensor problems caused by attacks, faults, saturation, etc. To address practical issues such as sampling intervals and limited computing and transmission resources, we propose a discrete sampling–event-triggered transmission–continuous estimation and control framework for continuous-time systems. Despite the presence of measurement interferences and potential corruption, the designed event-triggered mechanism and sensor fusion algorithm can be used to reduce data transmission and estimate the actual output, respectively. The designed adaptive observer can estimate continuous-time system states and parameters whether the output is obtained in a continuous, short-interval discrete or suitable event-triggered manner. This capability facilitates control design and real-time monitoring of vehicle states. Additionally, the presented backstepping control design and analysis method utilizing Levant differentiators can be applied in situations where the controlled system’s states possess at least first-order differentiability.