An adaptive law based online system identification of cropped delta unmanned aerial vehicles from flight tests

Abstract

Online system identification of an aircraft has multifaceted advantages, such as real-time health monitoring, fault detection and reconfiguration of control, to name a few. Researchers have utilized variants of the equation error method, which involves non-gradient-based computation to carry out online system identification, due to its feasibility for real-time implementation. However, it was found to be underperforming while handling high angles of attack as well as time-varying bias parameters. In the current manuscript, an attempt has been made to alleviate the aforementioned limitation by proposing a novel online system identification technique based on adaptive law and its applicability is demonstrated by using compatible flight data sets, acquired from two cropped delta wing unmanned aerial vehicles, pertaining to linear, moderately nonlinear, and near stall flight regimes. It is observed that the proposed method is able to predict unknown aerodynamic derivatives, irrespective of flight envelopes, if dynamic modes are adequately excited. Confidence in the estimated aerodynamic parameters is assured with the two-sigma error bound. It is noticed that the maximum relative standard deviation in the estimates obtained with the proposed method is less than the recursive least squares method and higher than the sequential least squares-recursive Fourier transform method in the linear flight regime. Simulated responses using the estimates obtained from aforementioned methods are compared with measured flight data, and it is witnessed that the simulated nondimensional lift coefficient with the proposed method has a relative offset of less than 34.2% w.r.t measured value in near-stall flight regimes, which is least among all three methods. © 2023 Elsevier Masson SAS