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- 저자 : Saderla, Subrahmanyam (검색결과 2 건)
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Online system identification of mini cropped delta UAVs using flight test methods
Saderla, Subrahmanyam,Kim, Yoonsoo,Ghosh, A.K. Elsevier 2018 Aerospace science and technology Vol.80 No.-
<P><B>Abstract</B></P> <P>The current manuscript presents the longitudinal and lateral directional online parameter estimation of two unmanned aerial vehicles (UAVs) using sequential Least Squares formulation in frequency domain. The two fixed wing UAVs share a similar cropped delta planform and differ in their cross sectional geometries, one with a rectangular and the other being a reflex airfoil cross sections respectively. Recursive Fourier Transform algorithm has been used to convert the flight data in time domain to frequency domain which is measured by means of a dedicated on-board data acquisition system capable of on-board logging and telemetry to ground station. The combination of Sequential Least Squares with Recursive Fourier Transform (SLS-RFT) in frequency domain can be used to carry out online parameter estimation. An attempt has been made to check the applicability of the current method to estimate parameters from the generated flight data of the two UAVs using both conventional as well as random control inputs. Results showed that the parameters estimated, using SLS-RFT, from the linear flight data are consistent and in close agreement with the obtained parameters from full scale wind tunnel testing of UAVs. It was also observed that the estimates from the manoeuvres with multistep control inputs converged faster compared to the parameters obtained from the manoeuvres with slow varying control surface deflections. The time varying linear aerodynamic parametric model of SLS-RFT was able to capture the dynamics of the flights with nonlinear aerodynamics. Certain limitations of the current online system identification method were also observed with estimating parameters from the flight data of UAVs performing near stall manoeuvres. The estimated parameters using SLS-RFT are also compared with the results obtained from batch methods namely classical Maximum Likelihood (ML) and neural based Neural–Gauss–Newton (NGN) methods.</P>
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적응 차수 조노토픽 칼만 필터를 활용한 비정상 비행상태 항공기의 실시간 시스템 식별
김성민(Seongmin Gim),헨드라 구남완 하노(Hendra G. Harno),수브라마냠 사델라(Subrahmanyam Saderla),김윤수(Yoonsoo Kim) 한국항공우주학회 2022 韓國航空宇宙學會誌 Vol.50 No.2
실속, 결빙, 센서 이상 등으로 인해 일어나는 제어불능 또는 비정상 비행 상황은 항공기의 추락으로 이어지기 때문에 필수적으로 대비해야 한다. 이와 관련해 비정상 비행 상황에서 항공기의 동적 특성을 정확하게 파악하는 것은 매우 중요하다. 본 논문에서는 일시적인 센서 이상이 발생한 고받음각의 비정상 비행상태 항공기에 대해서 기존의 칼만 필터 기반의 시스템 식별법 대비 보다 효과적인 적응 차수 조노토픽 칼만 필터와 이동창-최소자승법을 활용한 시스템 식별법을 제시하였다. 제안한 방법을 실제 비행 데이터에 적용하고 그 성능을 기존 연구 결과와 비교하였다. It is essential to prevent LoC(Loss-of-Control) or upset situations caused by stall, icing or sensor malfunction in aircraft, because it may lead to the crash of the aircraft. With this regard, it is crucial to correctly identify the dynamic characteristics of aircraft in such upset conditions. In this paper, we present a SID(System IDentification) method utilizing the moving-window based least-square and the adaptive-order ZKF(Zonotopic Kalman Filter), which is more effective than the existing Kalman-filter based SID for the aircraft in upset condition at a high angle of attack with temporary sensor malfunction. The proposed method is then tested on real flight data and compared with the existing one.
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