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Aeroelastic Control

  • Earl H. Dowell
Chapter
Part of the Solid Mechanics and Its Applications book series (SMIA, volume 217)

Abstract

Active control of aeroelastic systems is a subject of continuing interest and this chapter provides an introduction to this fascinating topic.

Keywords

Mode Shape Linear Matrix Inequality Delta Wing Linear Fractional Transformation Aerodynamic Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Meirovitch L (1980) Computational methods in structural dynamics. Sijhoff & Noordhoff, Rockville, MarylandGoogle Scholar
  2. 2.
    Noll TE (1993) Structural dynamics and aeroelasticity, vol 5 of flight-vehicle materials, structures, and dynamics—assessment and future directions, chapter Aeroservoelasticity, ASME, pp 179–212Google Scholar
  3. 3.
    Livne E (1997) Integrated aeroservoelastic optimization: Status and directions, In: 38th AIAA structures, structural dynamics and materials conference, April 1997Google Scholar
  4. 4.
    Perry BI, Cole SR, Miller GD (1995) Summary of the active flexible wing program. AIAA J Aircr 32(1):10–15CrossRefGoogle Scholar
  5. 5.
    Mukhopadhyay V (2000) Benchmark active control technology: part I. J Guid Control Dyn 23(5):913CrossRefGoogle Scholar
  6. 6.
    Kailath T (1980) Linear systems. Prentice-Hall, Englewood CliffsGoogle Scholar
  7. 7.
    Maciejowski JM (1989) Multivariable feedback design. Addison-Wesley, New YorkzbMATHGoogle Scholar
  8. 8.
    Ogata K (1997) Modern control engineering, 3rd edn. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  9. 9.
    Zhou K, Doyle JC (1998) Essentials of robust control. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  10. 10.
    Skogestad S, Postlethwaite I (1996) Multivariable feedback control: analysis and design. Wiley, ChichesterGoogle Scholar
  11. 11.
    Ljung L (1987) System identification: theory for the user. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  12. 12.
    Juang J-N (1994) Applied system identification. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  13. 13.
    Moore BC (1981) Principal component analysis in linear systems: controllability, observability and model reduction. IEEE Trans Autom Control 26(1):17–32CrossRefzbMATHGoogle Scholar
  14. 14.
    Theodorsen T (1935) General theory of aerodynamic instability and the mechanism of flutter. Technical report, NACAGoogle Scholar
  15. 15.
    Bisplinghoff RL, Ashley H, Halfman RL (1955) Aeroelasticity. Addison-Wesley, CambridgezbMATHGoogle Scholar
  16. 16.
    Sears WR (1940) Operational methods in the theory of airfoils in non-uniform motion. J Franklin Inst 230:95–111MathSciNetCrossRefGoogle Scholar
  17. 17.
    Jones RT (1940) The unsteady lift of a wing of finite aspect ratio. Technical report, NACAGoogle Scholar
  18. 18.
    Peters DA, Karunamoorthy S, Cao WM (1995) Finite state induced flow models; part I two-dimensional thin airfoil. J Aircr 32:313–322CrossRefGoogle Scholar
  19. 19.
    Edwards JW, Ashley H, Breakwell JV (1979) Unsteady aerodynamic modeling for arbitrary motions. AIAA J 17:365–374CrossRefzbMATHGoogle Scholar
  20. 20.
    Conner MD, Tang DM, Dowell EH, Virgin LN (1997) Nonlinear behavior of a typical airfoil section with control surface freeplay: a numerical and experimental study. J Fluids Struct 11:89–109Google Scholar
  21. 21.
    Tang DM, Dowell EH, Virgin JN (1998) Limit cycle behavior of an airfoil with a control surface. J Fluids Struct 12:839–858CrossRefGoogle Scholar
  22. 22.
    Baker ML, Mingori DL, Goggin PJ (1996) Approximate subspace iteration for constructing internally balanced reduced order models of unsteady aerodynamic systems. AIAA structures structural dynamics and materials conference. Salt Lake City, UtahGoogle Scholar
  23. 23.
    Holmes P, Lumley JL, Berkooz G (1996) Turbulence, coherent structures, dynamical systems and symmetry. Monographs on mechanics. Cambridge University Press, CambridgeGoogle Scholar
  24. 24.
    Tang DM, Kholodar D, Juang JN, Dowell EH (2001) System identification and proper orthogonal decomposition method applied to unsteady aerodynamics. AIAA J 39(8):1569–1576CrossRefGoogle Scholar
  25. 25.
    Hall KC (1994) Eigenanalysis of unsteady flows about airfoils, cascades, and wings. AIAA J 32(12):2426–2432CrossRefzbMATHGoogle Scholar
  26. 26.
    Tang DM, Klolodar D, Dowell EH (2000) Nonlinear response of airfoil section with control surface freeplay to gust loads. AIAA J 38(9):1543–1557CrossRefGoogle Scholar
  27. 27.
    Tang DM, Henry JK, Dowell EH (1999) Limit cycle oscillations of delta wing models in low subsonic flow. AIAA J 37(11):1355–1362CrossRefGoogle Scholar
  28. 28.
    Tang DM, Dowell EH (2001) Effects of angle of attack on nonlinear flutter of a delta wing. AIAA J 39(1):15–21CrossRefGoogle Scholar
  29. 29.
    Richard RE, Rule JA, Clark RL (2001) Genetic spatial optimization of active elements on an aeroelastic delta wing. ASME J Vib Acoust 123(4):466–471CrossRefGoogle Scholar
  30. 30.
    Clark RL, Gibbs GP, Saunders WR (1998) Adaptive structures, dynamics and control. Wiley, New YorkGoogle Scholar
  31. 31.
    Andersen BW (1954) Vibration of triangular cantilever plates by the ritz method. J Appl Mech 21:365–370zbMATHGoogle Scholar
  32. 32.
    Gere JM, Timoshenko SP (1990) Mechanics of materials, 3rd edn. PWS publishing, BostonGoogle Scholar
  33. 33.
    Hagood NW, Chung WH, Von Flotow A, Modeling of piezoelectric actuator dynamics for active structural control, In: Proceedings of the 31st AIAA/ASME/ASCE/AHS structures, structural dynamics and materials conference, 2–4 April , 1990, pp. 2242–2256 or AIAA-90-1087-CPGoogle Scholar
  34. 34.
    Richard R (2002) Optimized flutter control for an aeroelastic delta wing. PhD thesis, Duke UniversityGoogle Scholar
  35. 35.
    Cole DG, Clark RL (1994) Adaptive compensation of piezoelectric sensoriactuators. J Intell Mater Syst Struct 5:665–672CrossRefGoogle Scholar
  36. 36.
    Richard RE, Clark RL (2003) Delta wing flutter control using spatially optimized transducers J Intell Mater Syst Struct 14:677–691Google Scholar
  37. 37.
    Williams T (1990) Closed-form grammians and model reduction for flexible space structures. IEEE Trans Autom Control 35(3):379–382CrossRefzbMATHGoogle Scholar
  38. 38.
    Boyd S, Baratt C (1991) Linear controller design: limits of performance. Prentice-Hall, Englewood CliffsGoogle Scholar
  39. 39.
    Smith CC, Clark RL (1999) Tradeoffs in design complexity—temporal versus spatial compensation. J Sound Vib 228(5):1182–1194CrossRefGoogle Scholar
  40. 40.
    Clark RL, Cox DE (1999) Band-limited actuator and sensor selection for disturbance rejection. AIAA J Guid Control Dyn 22(5):740–743CrossRefGoogle Scholar
  41. 41.
    Lim KB (1997) Disturbance rejection approach to actuator and sensor placement. J Guid Control Dyn 20(1):202–204CrossRefzbMATHGoogle Scholar
  42. 42.
    Lim KB, Gawronski W (1996) Hankel singular values of flexible structures in discrete time. J Guid Control Dyn 19(6):1370–1377CrossRefzbMATHGoogle Scholar
  43. 43.
    Anderson B, Moore J (1990) Optimal control: linear quadratic methods., Information and system sciences seriesPrentice-Hall, Englewood CliffszbMATHGoogle Scholar
  44. 44.
    Vipperman JS, Clark RL, Conner M, Dowell EH (1998) Experimental active control of a typical section using a trailing-edge flap. J Aircr 35(2):224–229CrossRefGoogle Scholar
  45. 45.
    Vipperman JS, Clark RL, Conner M, Dowell EH (1999) Comparison of \(\mu \) and \(H_2\) synthesized controllers on an experimental typical section. AIAA J Guid Control Dyn 22(2):278–285CrossRefGoogle Scholar
  46. 46.
    Frampton KD, Clark RL (2000) Experiments on control of limit cycle oscillations in a typical section. AIAA J Guid Control Dyn 23(5):956–960CrossRefGoogle Scholar
  47. 47.
    Leith DJ, Leithhead WE (2000) Survey of gain-scheudling analysis and design. Int J Control 73(11):1001–1025CrossRefzbMATHGoogle Scholar
  48. 48.
    Packard A (1994) Gain scheduling via linear fractional transformations. Syst Control Lett 22:79–92MathSciNetCrossRefzbMATHGoogle Scholar
  49. 49.
    Apkarian P, Gahient P (1995) A convex characterization of gain-scheduled \(H_\infty \) controllers. IEEE Trans Autom Control 40(5):853–864CrossRefzbMATHGoogle Scholar
  50. 50.
    Nesterov Y, Nemirovsky A (1994) Interior-point polynomial methods in convex programming., Studies in applied mathematicsSIAM, PhiladelphiaCrossRefGoogle Scholar
  51. 51.
    Renegar J (2001) A mathematica view of interior-point methods in convex optimization., MPS-SIAM series on optimizationSIAM, PhiladelphiaCrossRefGoogle Scholar
  52. 52.
    Boyd S, ElGhaoui L, Feron E, Balakrishnan V (1994) Linear matrix inequalities in system and control theory., Studies in applied sciencesSIAM, PhiladelphiaCrossRefzbMATHGoogle Scholar
  53. 53.
    ElGhaoui L, Niculescu SI (eds) (2000) Advances in linear matrix inequality methods in control., Advances in design and controlSIAM, PhiladelphiaGoogle Scholar
  54. 54.
    Dullerud GE, Paganini F (1999) A course in robust control theory: a convex approach vol.36 of texts in applied mathematics. Springer, New YorkGoogle Scholar
  55. 55.
    Packard A, Zhou K, Pandey P, Becker G (1991) A collection of robust control problems leading to lmi’s, In: Proceedings of the 30th conference on decision and control, Brighton, England, December 1991, pp 1245–1250Google Scholar
  56. 56.
    Gahinet P (1994) Explict controller formulas for lmi-based \(H_\infty \) synthesis, In: Proceedings of the IEEE American Control conference, pp 2396–2400Google Scholar
  57. 57.
    Chiali M, Gahinet P (1996) \(H_\infty \) design with pole placement constraints: an lmi approach. IEEE Trans Autom Control 41(3):358–367CrossRefGoogle Scholar
  58. 58.
    Scherer C, Gahinet P, Chilali M (1997) Multiobjective output-feedback control via lmi optimization. IEEE Trans Autom Control 42(7):896–911MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Apkarian P, Adams RJ (1998) Advanced gain-scheduling techniques for uncertain systems. IEEE Trans Control Syst Technol 6(1):21–32CrossRefGoogle Scholar
  60. 60.
    Barker JM, Balas GJ (2000) Comparing linear parameter-varying gain-scheduled control techniques for active flutter suppression. J Guid Control Dyn 23(5):948–955CrossRefGoogle Scholar
  61. 61.
    Chiali M, Gahinet P (1999) Robust pole placement in lmi regions. IEEE Trans Auto Control 44(12):2257–2270CrossRefGoogle Scholar
  62. 62.
    Cox DE (2003) Control design for parameter dependent aeroelastic systems. PhD. thesis, Duke UniversityGoogle Scholar
  63. 63.
    Conner M (1996) Nonlinear aeroelasticity of an airfoil section with control surface freeplay. PhD thesis, Duke UniversityGoogle Scholar
  64. 64.
    Trickey ST (2000) Global and local dynamics of an aeroelastic system with a control surface freeplay nonlinearity. PhD thesis, Duke UniversityGoogle Scholar
  65. 65.
    Trickey ST, Virgin LN, Dowell EH (2002) The stability of limit-cycle oscillations in a nonlinear aeroelastic system. Proc Math Phy Eng Sci 458(2025):2203–2226MathSciNetCrossRefzbMATHGoogle Scholar
  66. 66.
    Overschee PV, Moor BD (1996) Subspace identification of linear systems: theory, implementation, applications. Kluwer Academic Publishers, DordrechtCrossRefGoogle Scholar
  67. 67.
    Sturm JF (1999) Using (SeDuMi 1.02), A (MATLAB) toolbox for optimization over symmetric cones. Optimization methods and software, pp 625–653Google Scholar
  68. 68.
    Juang JN, Pappa RS (1985) An eigensystem realization algorithm for modal parameter identification and model reduction. J Guid Control Dyn 8(5):620–627CrossRefzbMATHGoogle Scholar
  69. 69.
    Overschee PV, Moor BD (1994) N4sid: subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30:75–93CrossRefzbMATHGoogle Scholar
  70. 70.
    Tang DM, Dowell EH, Hall KC (1999) Limit cycle oscillations of a cantilevered wing in low subsonic flow. AIAA J 37(3):364–371CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Mechanical Engineering and Materials ScienceDuke UniversityDurhamUSA

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