Adaptive Kalman Filter for Automated Actuator Fault Diagnosis in Unmanned Surface Vehicle
摘要
Actuator systems in unmanned surface vehicles (USV) are prone to failure. To guarantee safe and successful autonomous operations, actuator systems must be monitored. However, sensors monitoring actuator systems are often unavailable. Hence, the actuator fault must be estimated. This paper presents an adaptive Kalman filter (AKF) algorithm for actuator fault estimation in USV based on position, velocity, and orientation sensors. Numerical results show the benefit of using the AKF. Furthermore, the presented method is validated using a USV with an actuator fault in the experiment.