By Edgar Brannon
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Additional resources for Fokker D. VII
System identification and simulation modeling can be seen as inverse procedures, as shown in Fig. 6. In system identification the aircraft response is measured, and a dynamic model is extracted from the data, the reverse of developing a simulation. This extracted model can be compared directly with the simulation model, thereby providing greatly increased physical understanding about the nature of the characteristics of the physical systems that are being modeled. One role of system identification is to quantify where the actual and predicted motions do not match and to provide information on proper tuning for improved prediction accuracy.
S. S. and international industry and research colleagues. The support and encouragement of the AFDD Director, Andrew W. Kerr, and the Chief of the AFDD Flight Control and Cockpit Integration Branch, Barry R. Lakinsmith, has made possible the long-term pursuit of this research topic. Their support and encouragement for writing this book is also greatly appreciated. M. Leung, Mavis G. Cauffman, Gary L. Villere, Lawrence E. Pierce, Dexter L. Hermstad, and Paul S. Salchak. A special thanks go to my father, Morris Tischler, for his lifelong encouragement and for being my greatest fan.
System-identification projects have ranged from small (9-in. diam) ducted-fan unmanned air vehicles (UAVs) to large (100-ft wing span) solar-powered UAVs, including a wide range of manned fixed-wing aircraft and rotary-wing aircraft (rotorcraft) configurations. , actuators and other flight control components). Examples are given in the overview of Sec. 4 and throughout the book. The identification of dynamics models of flight vehicles from flight-test data is made difficult by many factors, as explained in this book.