Estimation of the operating state of intelligent connected vehicles based on optimized unknown input observers

With the continuous development of intelligent technology, the automotive industry is transforming intelligent connected vehicles. Intelligent connected vehicles can obtain information about their surroundings through sensors and communication technologies, enabling autonomous driving and vehicle-to-vehicle collaboration with high safety and comfort. Accurately estimating the vehicle's operating state is a crucial technology to realize autonomous driving. It has been studied extensively, including methods such as using Kalman filters to detect faults and attacks in intelligent grids and applying traditional Kalman filtering and cumulative fault detection methods to online detection of network attacks. However, most of the unknown signals studied are divided into random or interval signals, and the observers are mainly point estimation observers and interval observers. This paper proposes a parameter design method based on Luenberger-type interval observers for singular systems with bounded uncertainty, which considers complex interferences. The proposed method has potential applications in monitoring and isolating defects in intelligent networked vehicles.