Mathematical models for the dynamics of the DLR RO 105 helicopter arc extracted from night test data using two different approaches: frequency-domain and time-domain identiliration. Bath approaches arc reviewed. Results fmm an extensive data consistency analysis are given. Identifications for 6-degree-of-heedom (DOF) rigid-body models are presented and compared in detail. The extracted models compare favorably and their prediction capability is demonstrated. Approaches lo extcnd the 6-DOF models are addressed and first results are presented.
A fourteen degree offreedom model which characterizes the open loop UH-60 flight dynamics in hover is identified from flight test data using a frequeney-response-error identification method. The model includes rigid body fuselage dynamics, regressing rotor flap and lead-lag dynamics, main rotor inflow, rotor RPM, and enginelgovernor dynamics and is applicable in the frequency range of 0.1 to 20 radlsec. Thestahilityand control derivativemodel is iteratively fit to a set offlight identified frequency responses. Parameters areiteratively eliminated from the model structure based on robust metrics of parameter insensitivity and correlation. The final minimally parameterized model is driven with dissimilar flight test measured inputs and the outputs are compared to the flight measured responses to verify that the model adequately characterizes the aircraft dynamic response to pilot inputs in the frequency range of interest and has good predictive capabilities. When the model parameters are compared with theoretical results, the identified flapping dynamics are in accord with theory except for the coupling terms. When the time and frequency responses of the model are compared to those of two blade element simulation models of the UH-60, the identified model predicts the on-axis response of the helicopter as well as the other models and has superior off-axis fidelity.
A critical element of rotorcraft autonomy is a flight control system that can operate harmoniously with the various autonomy components that depend on it. This is particularly true for highly interactive components, such as obstacle field navigation (OFN), where the vehicle navigation course is constantly being altered as more terrain information is gathered. This paper describes the development, integration, and flight-testing of an autonomous flight control system (AFCS) on a JUH-60A research helicopter. Flight-test results include control law validation using frequency domain analysis and performance characteristics using both ADS-33E mission task elements and path-error measurements. These performance data are then used to configure a risk minimizing OFN algorithm with the AFCS. The integrated OFN algorithm and AFCS are demonstrated in flight by navigating autonomously through 23 mi of mountainous terrain.
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