Comparison of black-grey-white box approach in system identification of a flight vehicle

Developing an autonomous flight control system for a fixed-wing unmanned aerial vehicle (UAV) requires the mathematical representation of the system dynamics which can be obtained through system identification, such as using white, grey or black box methods. In this paper, three system identification methods; white method modeling using Cook formulas which is used analytically to develop the linear-time-invariant model structure with the aerodynamic coefficients of the UAV extracted from Missile DATCOM computations at certain flight conditions, and two empirical mathematical models constructed from non-linear flight simulator defined as the Greyification of Black Box Model (B2G) and Direct Grey to White Linkage (G2W). The comparison shows that both empirical methods prove to be very instrumental to contrast and produce higher sense of engineering than the analytical one

[1]  Zeashan Hameed Khan,et al.  Computational Intelligence for Decision Support in Cyber-Physical Systems , 2014, Computational Intelligence for Decision Support in Cyber-Physical Systems.

[2]  Ruxandra Botez,et al.  Cessna Citation X Airplane Grey-Box Model Identification without Preliminary Data , 2014 .

[3]  R. M. Botez,et al.  Vertical profile optimization for the Flight Management System CMA-9000 using the golden section search method , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[4]  Ruxandra Mihaela Botez,et al.  New Approach for the Identification and Validation of a Nonlinear F/A-18 Model by Use of Neural Networks , 2010, IEEE Transactions on Neural Networks.

[5]  Heeyoung Kim,et al.  A new metric of absolute percentage error for intermittent demand forecasts , 2016 .

[6]  Konstantinos Kanistras,et al.  System Identification of Circulation Control UAV Using X-Plane Flight Simulation Software and Flight Data , 2017 .

[7]  Alex G. Sim.  Flight characteristics of a modified Schweizer SGS1-36 sailplane at low and very high angles of attack , 1990 .

[8]  Ruxandra Botez,et al.  Identification of a non-linear F/A-18 model by the use of fuzzy logic and neural network methods , 2011 .

[9]  Jiraphon Srisertpol,et al.  Implementation of system identification and flight control system for UAV , 2017, 2017 3rd International Conference on Control, Automation and Robotics (ICCAR).

[10]  Caleb Phillips,et al.  Flight Test Evaluation and System Identification of the Area-I Prototype-Technology-Evaluation Research Aircraft (PTERA) , 2014 .

[11]  Timothy R. Moes,et al.  Results From F-18B Stability and Control Parameter Estimation Flight Tests at High Dynamic Pressures , 2000 .

[12]  Tom S Richardson,et al.  Unmanned Aerial Vehicle Flying Qualities , 2008 .

[13]  Sean M. Calhoun.  Six Degree-of-Freedom Modeling of an Uninhabited Aerial Vehicle , 2006 .

[14]  Rini Akmeliawati,et al.  Hybrid DE-PEM algorithm for identification of UAV helicopter , 2014 .

[15]  Ruxandra Botez,et al.  Identification of a MIMO state space model of an F/A-18 aircraft using a subspace method , 2009, The Aeronautical Journal (1968).