Research of online identification of UAV using extended Kalman filter
In order to implement PID control with adaptive gain and better track tracking task,an online identification method based on extended Kalman filter is proposed.Firstly,a discrete-time model of six rotor UAV is established,and the nonlinear characteristics of UAV control system are considered.Then,according to the nonlinear discrete-time model of UAV,a neural estimator based on extended Kalman filter is developed.The state estimation is used to complete on-line identification,so that the PID gain can be adjusted to complete the tracking task.Experimental results verify the effectiveness of the proposed method.The proposed identification model can be applied to nonlinear and unknown dynamic systems,and has certain practicability.
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