Adaptive event-triggered dynamic output feedback control for networked autonomous vehicles affected by signal quantization and actuator failures

In this paper, an event-triggered sliding-mode output feedback control (SMOFC) strategy with adaptive fault-tolerance is proposed to achieve path tracking for networked autonomous vehicles, considering the challenges posed by network communication and electric steering systems. Initially, signal quantization, external disturbances, and actuator faults are incorporated into the vehicle model. This incorporation enhances the designed controller’s robustness against a broader and more demanding range of driving scenarios. Subsequently, in situations where only output feedback is available, a dynamic output compensator is designed to reconstruct the unmeasurable vehicle state. Utilizing the reconstructed vehicle state, an event-triggered strategy is devised to alleviate the network burden and determine the minimum time between triggering events to prevent Zeno behavior. Furthermore, an adaptive mechanism is employed to estimate the actuator fault boundaries. The performance of the designed controller is evaluated through simulation instances.