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Derivation of control activity metrics for the rule-based prediction of helicopter pilot workload

Published online by Cambridge University Press:  03 February 2016

C. A. Macdonald
Affiliation:
School of Computing and Mathematical Sciences, Glasgow Caledonian University, Glasgow, UK
R. Bradley
Affiliation:
School of Computing and Mathematical Sciences, Glasgow Caledonian University, Glasgow, UK

Abstract

Control activity is a recognised gauge of pilot workload and recent research has employed wavelet decomposition to classify discrete control actions into categories such as guidance and stabilisation. The aim of the present work is to extend the wavelet approach so that workload may be quantified through sets of rules based on appropriate control activity metrics. The rules are derived from data collected in piloted simulation trials of a variety of flying tasks involving a number of pilots and different helicopter configurations. Statistical tests are then applied which test the efficacy of the derived rules. The immediate aim of the research is to establish whether workload can be successfully predicted from control responses. The underlying goal however, is to be able to predict workload ratings from desktop simulations in order to provide indicative workload information at the design stage. The contribution of the current study to this objective is discussed.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2004 

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References

1. Anon Aeronautical Design Standard, Performance Specification Handling Qualities Requirements for Military Rotorcraft, ADS-33E. US Army Aviation and Missile Command, March 2000.Google Scholar
2. Turner, G.P., Bradley, R. and Brindley, G. Simulation of pilot control activity for the prediction of workload ratings in helicopter/ship operations. 26th European Rotorcraft Forum, The Hague, The Netherlands, September 2000.Google Scholar
3. Rowe, S.J. et al The response of helicopters to aerodynamic disturbances around offshore helidecks. Royal Aero Soc Conf on Helicopter Operations in the Maritime Environment. 2001.Google Scholar
4. Macdonald, C.A. The Development of an Objective Methodology for the Prediction of Helicopter Pilot Workload, PhD Thesis, Department of Mathematics, Glasgow Caledonian University, Scotland, UK, January 2001.Google Scholar
5. Thomson, D.G. and Bradley, R. The use of inverse simulation for preliminary assessment of helicopter handling qualities, Aeronaut J, August/September 1997, 101, (1007), pp 287294.Google Scholar
6. Hess, R.A., Zeyada, Y. and Heffley, R.K. Modeling and simulation for helicopter task analysis, J American Helicopter Soc, October 2002, 47, (4), pp 243252.Google Scholar
7. Thomson, D.G. and Bradley, R. The principles and practical application of helicopter inverse simulation, Simulation practice and theory: Int J Federation of European Simulation Socs, 1998, 6, pp 4770.Google Scholar
8. Bradley, R. and Brindley, G. Progress in the development of a robust pilot model for the evaluation of Rotorcraft, 28th European Rotorcraft Forum, Bristol, UK, October 2002, Paper 46.Google Scholar
9. Padfield, G.D. Helicopter Flight Dynamics: The Theory and Application of Flying Qualities and Simulation Modelling, Blackwell Science, Cambridge, UK, 1995.Google Scholar
10. Howell, S.E. Preliminary Results from Flight and Simulation Trials to Investigate Pilot Control Workload in Slalom Manoeuvres, Flight Dynamics and Simulation Department, Defence Research Agency, Bedford, UK, DRA/AS/FDS/WP95181/1, June 1995.Google Scholar
11. Padfield, G.D., Charlton, M.T. and McCallum, A.T. The Fidelity of HiFiLynx on the DERA Advanced Flight Simulator using ADS-33 Handling Qualities Metrics: Report on the CONDVAL Trial – Flying Lynx in Good Visual Environment, Flight Management and Control Department, Defence Evaluation and Research Agency, Bedford, DRA/AS/FDS/TR96103/1, December 1996.Google Scholar
12. Charlton, M.T. and Howell, S.E. Trial Specification for AFS Simulation Trial TWIN3, Draft Version, Flight Management and Control Department, Defence Evaluation and Research Agency, Bedford, UK, Ref: DERA/AS/FDS/2TG5/22/01T/97/08, November 1997.Google Scholar
13. Padfield, G.D. A Theoretical Model of Helicopter Flight Mechanics for Application to Piloted Simulation, RAE TM 81048, April 1981.Google Scholar
14. Jones, J.G., Padfield, G.D. and Charlton, M.T. Wavelet analysis of pilot workload in helicopter low-level flying tasks, Aeronaut J, January 1999, 103, (1019), pp 5563.Google Scholar
15. Padfield, G.D. et al Where does the workload go when pilots attack manoeuvres?, 20th European Rotorcraft Forum, Amsterdam, The Netherlands, 1994.Google Scholar
16. Foster, G.W. and Jones, J.G. Analysis of atmospheric turbulence measurements by spectral and discrete gust methods, Aeronaut J, May 1989, 93, (925), pp 162175.Google Scholar
17. Macdonald, C.A. and Bradley, R. An initial investigation of helicopter pilot workload rating prediction using rule induction techniques, Technical Report, TR/MAT/CMacD/RB/97-65, Glasgow Caledonian University, June 1997.Google Scholar
18. Padfield, G.D., Charlton, M.T. and Kimberley, A.M. Helicopter flying qualities in critical mission task elements: initial experience with the DRA (Bedford) Large Motion Simulator, 18th European Rotorcraft Forum, Avignon, France, 1518 September 1992.Google Scholar
19. Thomson, D.G and Bradley, R. Prediction of the dynamic characteristics of helicopters in constrained flight, Aeronaut J, December 1990, 94, (940), pp 344354.Google Scholar
20. Breiman, L. et al Classification and Regression Trees, Chapman and Hall, New York 1993.Google Scholar
21. Bain, C. et al Inductive Rule Learning – Pilot Assessment, Glasgow Caledonian University. Final Report, DRA Contract ASF/2650, 1996.Google Scholar
22. Daniel, W.W. Applied Nonparametric Statistics, PWS-KENT Publishing Company, Boston Massachusetts, USA, 1990.Google Scholar
23. Armitage, P. and Berry, G. Statistical Methods in Medical Research, Blackwell Science, Third Edition, 1994 Google Scholar
24. Lindley, D.W. and Scott, W.F. New Cambridge Elementary Statistical Tables, Cambridge University Press, 1984.Google Scholar
25. Mcruer, D.T. and Krendel, E.S. Mathematical Models of Human Pilot Behaviour. AGARD-AG-188, January 1974.Google Scholar