Abstract
A mathematical model of the dive phase is an important research content for improving the accuracy of terminal control in the small unmanned aerial vehicle. The acquisition of the diving model poses new challenges, such as the small installation space, ultra-low flying height of small suicide drones, short flight time, strong coupling, less observable measurement, and elastic deformation of the wings during the drone dive phase. Based on the autoregressive moving average method, a multi-input multioutput noise term topology mathematical model is proposed in this paper. Through an improved least squares identification method, the diving model in the flight test is analyzed and verified. The identification results of the diving model obtained by the proposed method are compared with the least squares method dive model. The results indicate that the mathematical model and identification method proposed in this paper can effectively obtain the parameters of the drone dive model.
Highlights
With the development of SUAV technology, the function of unmanned aerial vehicles (UAVs) has gradually expanded from a single function of scouting, interference, and damage assessment to the integration of scouting and strikes
By utilizing the system response to specific input single utilization, the system identification method can rapidly and effectively acquire the mathematical model of the entire air vehicle, which has been extensively applied during flight tests of conventional air vehicles [2]
Results and Discussion rough the model loss functions corresponding to different parameter combinations comparison, the model structure of the SUAV in the diving phase can be written as
Summary
With the development of SUAV technology, the function of unmanned aerial vehicles (UAVs) has gradually expanded from a single function of scouting, interference, and damage assessment to the integration of scouting and strikes. E ARMA model is a common and high-precision time-series short-term prediction method It exhibits extensive applications in many domains such as fault detection, controller model identification, sensor correction, and structural testing and identification [30,31,32,33]. Bnb is a 2 ∗ 2 matrix to be identified, and V(k) denotes a 2 ∗ 2 colored noise matrix in the model indicating interference factors such as wind gusts and temperature during the test. Erefore, given that an SUAV is a multi-input multioutput coupling system during the diving phase, while the colored noise imposed considerable disturbance, a multivariable recursive augmented least squares identification method is derived in this paper. For the identification performance analysis, the following multi-input multioutput simulation model was established:. A four-displacement register with an input amplitude of 1 is used for the signal input
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
