Parameter Estimation of Unmanned Vehicle Based on ESO and EKF Algorithm
摘要
In this paper, the problem of parameter estimation of nonlinear unmanned vehicle systems is studied. By introducing an extended state to model the unknown parameters, the parameter estimation is realized by designing the extended state observer (ESO), and the influence of noise is tackled through extended Kalman filter (EKF). The observability is analyzed, and simulation example shows the effectiveness of the proposed parameter estimation method.