A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification
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Remote sensing has traditionally be done with satellites and manned aircraft. While these methods can yield useful scientific data, satellites and manned aircraft have limitations in data frequency, process time, and real time re-tasking. Small low-cost unmanned aerial vehicles (UAVs) can bridge the gap for personal remote sensing for scientific data. Precision aerial imagery and sensor data requires an accurate dynamics model of the vehicle for controller development. One method of developing a dynamics model is system identification (system ID). The purpose of this paper is to provide a survey and categorization of current methods and applications of system ID for small low-cost UAVs. This paper also provides background information on the process of system ID with in-depth discussion on practical implementation for UAVs. This survey divides the summaries of system ID research into five UAV groups: helicopter, fixed-wing, multirotor, flapping-wing, and lighter-than-air. The research literature is tabulated into five corresponding UAV groups for further research.
KeywordsSystem identification UAV Helicopter Fixed-wing Multirotor Flapping-wing Lighter-than-air Least squares Levenberg Marquardt Kalman filter EKF UKF Observer/Kalman identification Autoregressive exogenous inputs ARMAX Box Jenkins Prediction-error method Output-error method Neural network Fuzzy identification Time domain Frequency domain State-space Subspace CIFER Personal remote sensing
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