Abstract
This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.
Highlights
Unmanned Aerial Vehicles (UAVs) with rotary wings allow great flexibility of movements, which makes them very useful for several tasks and applications
In order to test the proposed Extended Kalman Filter (EKF)-based model parameter identification approach, under all the observability configurations defined in Section 4, a multi-rotor system as described in Figure 2 was simulated using Simulink R from MATLAB R
In order to investigate the theoretical conditions that were necessary for estimating the model parameters from different sets of sensor measurements, a nonlinear observability analysis was carried out
Summary
Unmanned Aerial Vehicles (UAVs) with rotary wings (multi-rotors) allow great flexibility of movements, which makes them very useful for several tasks and applications. In this case, one of the main objectives of the research community has been the improvement of the autonomy of these systems. One of the main objectives of the research community has been the improvement of the autonomy of these systems In this context, in order to develop autonomous control systems that perform well in terms of robustness, stability, precision, and adaptability, it is fundamental to have mathematical models that represent the actual dynamic behavior of the UAV in a precise manner
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