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Flight data reduction methodology for performance evaluation and comparison of model-following adaptive control laws

Published online by Cambridge University Press:  03 February 2016

M. G. Perhinschi  and
M. R. Napolitano
M. G. Perhinschi
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, West Virginia, USA
M. R. Napolitano
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, West Virginia, USA
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Abstract

Even small differences in atmospheric and/or flight conditions can potentially impact significantly the evaluation of the performance of the control laws and prevent a correct comparison, especially in the case of reduced size aircraft (autonomous or remotely piloted). Consistent deterministic control inputs can only be guaranteed through some form of computer-based on-board excitation system. In this paper, a methodology is proposed for flight data reduction with the purpose of accounting for non-homogeneous atmospheric conditions and inconsistent pilot inputs. The method is developed for the specific purpose of comparing model-following adaptive control laws. Performance evaluation parameters based on angular rate tracking errors are defined and used for the comparison. As a result of this approach, an additive correction is applied to the angular rate measurements to compensate for non-homogeneous turbulence effects. A multiplicative correction factor is applied to the angular rate tracking error to take into account non-identical pilot inputs. The procedure is validated with simulation and flight data obtained in the process of designing a set of fault tolerant control laws based on non-linear dynamic inversion with neural network augmentation for the reduced size WVU YF-22 aircraft model.

Type
Research Article
Information
The Aeronautical Journal , Volume 111 , Issue 1126 , December 2007 , pp. 807 - 814
Copyright
Copyright © Royal Aeronautical Society 2007 

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References

1. Olson, W.M., Aircraft Performance Flight Testing, June 2003, Technical information handbook, Air Force Flight Test Center, CA, USA.Google Scholar
2. Kimberlin, R.D., Flight Testing of Fixed-Wing Aircraft, 2003, AIAA Education Series.Google Scholar
3. US Department of Defense, Military specification – flying qualities of piloted airplanes, 1980, MIL-F8785C.Google Scholar
4. Wan, S., Campa, G., Yu, G., Seanor, B., Gururajan, S. and Napolitano, M.R., Development of formation control laws for the WVU YF-22 aircraft models, 2003, American Control Conference, 4-6 June 2003, Denver, CO, USA.Google Scholar
5. Seanor, B., Campa, G., Gu, Y., Napolitano, M.R., Rowe, L. and Perhinschi, M.G., Formation Flight Test Results for UAV Research Aircraft Models, 2004, AIAA2004-6251, AIAA Intelligent Systems Technical Conference, Chicago, IL, USA.Google Scholar
6. Perhinschi, M.G., Napolitano, M.R., Campa, G., Seanor, B. and Gururajan, S., Design of intelligent flight control laws for the WVU F-22 model aircraft, 2004, AIAA2004-6282, AIAA Intelligent Systems Technical Conference, Chicago, IL, USA.Google Scholar
7. Tischler, M.B. (Ed) Advances in Aircraft Flight Control, 1996, CRC Press.Google Scholar
8. Duda, H., Bouwer, G., Bauschat, J.M. and Hahn, K.U.. A review of the model following control approach, Robust Flight Control – A Design Challenge Lecture Notes in Control and Information Sciences, 1997, pp 116–124, Springer-Verlag, London, UK.Google Scholar
9. Calise, A.J. and Sharma, M.. Adaptive autopilot design for guided munitions, AIAA J Guidance, Control and Dynamics, 2000, 23, (5).Google Scholar
10. Kaneshige, J., Bull, J. and Totah, J.J., Generic neural flight control and autopilot system, August 2000, AIAA Paper 00-4281, Denver, CO, USA.Google Scholar
11. Deets, D.A. and Edwards, J.W., A remotely augmented vehicle approach to flight testing RPV control systems, November 1974, NASA TM X-56029, NASA Flight Research Center Edwards, CA, USA.Google Scholar
12. Del Frate, J.H. and Cosentino, G.B., Recent flight test experience with uninhabited aerial vehicles at the NASA Dryden Flight Research Center, April 1998, NASA/TM-1998-206546, Dryden Flight Research Center Edwards, CA, USA.Google Scholar
13. Anon, , Military specification, Flying qualities of piloted airplanes, November 1990, MILF-STD-1797A, US Department of Defense.Google Scholar
14. Perhinschi, M.G., Napolitano, M.R., Gururajan, S., Seanor, B. and Burken, J., Data reduction issues for performance evaluation and comparison of flight control laws, 2006, AIAA Atmospheric Flight Mechanics Conference, Keystone, CO, USA.Google Scholar
15. Perhinschi, M.G., Napolitano, M.R., Campa, G. and Fravolini, M.L., A simulation environment for testing and research of neurally augmented fault tolerant control laws based on non-linear dynamic inversion, 2004, AIAA Modeling and Simulation Technologies Conference, Providence, RI, USA.Google Scholar
16. Perhinschi, M.G. and Napolitano, M.R., Analysis of neural augmentation performance for fault tolerant control laws based on flight tests, August 2007, AIAA Guidance, Navigation and Control Conference, Hilton Head, SC, USA.Google Scholar