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Meeting	ACGS Committee Meeting 95 - Salt Lake City - March 2005
Agenda Location	6 SUBCOMMITTEE D - DYNAMICS, COMPUTATIONS 6.2 A Comparison of LPV, NLPV and CIFER Models or Rotary Wing UAVs
Title	A Comparison of LPV, NLPV and CIFER Models or Rotary Wing UAVs
Presenter	Richard Colgren
Affiliation	University of Kansas
Available Downloads*	presentation
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Abstract	The topics discussed in this presentation address the current research being conducted at the University of Kansas in the areas of unmanned aerial vehicle (UAV) dynamic model development, instrumentation, and flight test. This presentation specifically identifies the instrumentation currently used to record dynamic variables in remotely piloted vehicles and the software tools being used to generate these models. The UAVs covered in this presentation are the Raptor 50 and Raptor 50 V2 helicopters, with a brief mention of current work on the Yamaha RMAX helicopter. Two methods for the dynamic modeling of these remotely piloted vehicles are presented. A decoupled, three degrees of freedom linear parameter varying (LPV) theory-based longitudinal dynamics model of the Raptor 50 V2 helicopter was created within The MathWorks’ Matlab environment. The option to simulate a linear time invariant (LTI) model was also presented. Nonlinear and coupling terms are being incorporated within a nonlinear linear parameter varying NLPV model. The second method discussed uses the Comprehensive Identification from FrEquency Response (CIFER) software system. The CIFER program is an integrated facility for system identification based on a comprehensive frequency response approach. The methods used to develop a CIFER database were reported. These methods can produce a high quality extraction of complete multi-input and multi-output (MIMO) nonparametric frequency responses. These responses characterize the full characteristics of the system without a-prior model form assumptions. High fidelity models of these aerial vehicles are important in the understanding of vehicle dynamic response to control inputs. This research will be applied to robust autonomous control of these classes of vehicles.