BibTex RIS Kaynak Göster

Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System

Yıl 2011, Cilt: 24 Sayı: 4, 781 - 789, 16.12.2011

Öz

Aircraft dynamics are in general nonlinear, time varying, and uncertain. A control system (classical control systems) designed for a flight condition, may not provide the desired stability and performance characteristics in case of deviation from the equilibrium point. There are numerous studies regarding flight control in the literature. One of them is fuzzy flight control system Fuzzy Logic Controllers (FLCs) from their inception have demonstrated a vast range of applicability to processes where the plant transfer function is not defined but the control action can be described in terms of linguistic variables. FLCs are also being used with improved performance instead of “classical" controllers where the plant transfer function is known. Most of the applications about the design of fuzzy flight control are in simulation level. In this study, the design of classical and fuzzy PD controller for the pitch angle control system is analyzed and the results are compared for a very large, four-engine passenger jet aircraft.

 

 

                Key Words:  Aircraft, Classical PD controller, Flight control,

               Fuzzy PD controller.

 

Kaynakça

  • [1] Kahvecioglu, A., ”Flight control system design using multi-model approach and genetic algorithm”, Phd. Thesis, University of Anadolu, Eskisehir, (2000).
  • [2] Savran, A., Tasaltin, R., Becerikli, Y., “Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks”, ISA Transactions, 45(2): 225–247 (2006).
  • [3] Verbruggen, H.B., Zimmerman, H.J., Babuska, R. , “Fuzzy Algorithms for Control”, Kluwer Academic Publishers, Massachusetts, U.S.A, (1999).
  • [4] Robust Flight Control, A Design Challenge, Lecture Notes in Control and Information Sciences, Springer Berlin-Heidelberg, (224/1997).
  • [5] Aström, K.J., Wittenmark, B., “Adaptive control”, Addison Wesley, Lund Institute of Technology, U.S.A, (1989).
  • [6] Experience with the X-15 adaptive flight control system, http://www.nasa.gov/centers/dryden/pdf/87785mai n_H-618.pdf
  • [7] Enns, D.F., “Robustness of dynamic inversion vs. Mu synthesis: Lateral-directional flight control example”, Proceedings of the AIAA Conference on Guidance, Navigation, and Control, Portland, Oregon, (1990).
  • [8] Hayes, J.R., Bates, D.G., “LFT-based uncertainty modeling and mu-analysis of the HIRM+RIDE flight control law”, Proc. of the IEEE International Symposium on Computer Aided Control Systems Design (CACSD’02), Glasgow, 242–247, (2002).
  • [9] AI-Malki, M.F., Wei, G.D., “Advantages of using µ-synthesis for fault-tolerant flight control system”, IFAC Fault Detection, Supervision and Safety of Technical Processes, Beijing, Elsevier IFAC publication (2006).
  • [10] Nichols, R.A., Reichert R.T., “Gain Scheduling for H-Infinity Controllers: A Flight Control Example”, IEEE Transactions on Control Systems Technology, 1(2): 69-79 (1993).
  • [11] Kung, C.C., “Nonlinear H∞ robust control applied to F-16 aircraft with mass uncertainty using control surface inverse algorithm”, Journal of the Franklin Institute, 345: 851–876 (2008).
  • [12] Postlethwaitea, I., Prempain, E., Turkoglu, E., Turnera, M.C., Ellisb, K., Gubbelsb, A.W., “Design and flight testing of various H∞ controllers for the Bell 205 helicopter”, Control Engineering Practice, 13: 383–398, (2005).
  • [13] Ackermann, J., “Multi-Model Approaches to Robust Control System Design”, Lecture Notes in Control and Information Science, Springer Verlag ,70 (1985).
  • [14] Magni, J.F., “Multimodel eigenstructure assignment in flight control design”, Aerospace Science and Technology Elseiver Science, 3(3): 141-151 (1999).
  • [15] Li, Y., “Neuro-controller design for nonlinear Fighter aircraft maneuver using fully tuned RBF networks”, Automatica, 37: 1293-1301 (2001).
  • [16] Vijaya, K.M., “A direct adaptive neural command controller design for an unstable helicopter”, Engineering Applications of Artificial Intelligence, 22: 181–191 (2009).
  • [17] Savran, A., “Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks”, ISA Transactions, 45(2): 225–247 (2006).
  • [18] Wu, S.L., Chena, P.C., Hsu, C.H. , Chang, K.Y., “Gain-scheduled control of PVTOL aircraft dynamics with parameter-dependent disturbance”, Journal of the Franklin Institute, 345: 906–925 (2008).
  • [19] Yang, C.D., Luo, C.C, Shiu-Jeng, L., Chang, Y. H., “Applications of Genetic-Taguchi Algorithm in Flight Control Designs”, Journal of Aerospace Engineering, 18(4): 232-241 (2005).
  • [20] Fantinutto, R., Guglieri, G., Quagliotti, F.B., “Flight control system design and optimization with a genetic algorithm”, Aerospace Science and Technology, 9: 73–80 (2005).
  • [21] Jamshidi, M., Titli, A., Zadeh, L., Boverie, S., “Applications of fuzzy logic towards high machine intelligence quotient systems”, Prentice Hall PTR, New Jersey, (1997).
  • [22] Kantaa, A.F., Montavon, G., Planche, M.P., Coddet, C., “In-flight particle characteristics control by implementing a fuzzy logic controller”, Surface & Coatings Technology, 202: 4479–4482 (2008).
  • [23] Cordon, O., Gamide, F., Herrera, F. , Magdelen, H.L.,, “Ten Years of Genetic Fuzzy Systems Current Framework and New trends”, Fuzzy Sets and Systems, 141(1): 5-31 (2004).
  • [24] Sanchez, E., Shibata, T., Zadeh, L.A., “Genetic Algorithms and Fuzzy Logic Systems, Soft Computing Perspectives”, World Scientific, Singapore, (1997).
  • [25] Kim, M.S., “Fuzzy Controller Design with the Degree of Non-uniformity for the Scaled Active Steering Testbed in the Railway Vehicle”, Wseas Transactions on Systems and Control, 4(7): 306- 315 (2009).
  • [26] Popescu, M.C., Petrisor, A., Drighiciu, A., “Fuzzy Control of the Position for the Piston of an Industrial Robot”, 12th Wseas International Conference on Systems, Heraklion, Greece, 222- 226 (2008).
  • [27] Susnea, I., Vasiliu, G., Filipescu, A., ”Real-time, embedded fuzzy control of the Pioneer3-DX robo for path following”, 12th Wseas International Conference on Systems, Heraklion, Greece, 334- 338 (2008).
  • [28] Kiyak, E., “Flight control applications with fuzzy logic method”, MSc. Thesis, University of Anadolu, Eskisehir, (2003).
  • [29] Rotton, S.K., Brehm, T., Sandhu, G. S., “Analysis and Design of a Proportional Fuzzy Logic Controller”, Department of Electrical Engineering Wright State University, Dayton, Ohio (2005).
  • [30] Livchitz, M., Abershitz, A., Soudak, U., “Development of an automated fuzzy-logic- based expert system for an unmanned landing”, Fuzzy Sets and Systems, 93(2): 145-159, (1998).
  • [31] Beringer, D. B., Applying Performance-Controlled Systems, Fuzzy Logic, and Fly-By-Wire Controls to General Aviation, Civil Aerospace Medical Institute Federal Aviation Administration Oklahoma City, 2002, http://www.hf.faa.gov/docs/508/docs/cami/0207.pd f
  • [32] Işık, Y., Kahvecioğlu, A., Korul, H., ”Bir uçağın yunuslama açısı kontrolünde bulanık PD denetleyici performansının incelenmesi”, Kayseri VI. Havacılık Sempozyumu, Nevşehir, Türkiye, 434-438 (2006),
  • [33] Topuz, V., “Fuzzy genetic process control”, PhD. Thesis, University of Marmara, Istanbul, (2002). [34] Ross, T.J., “Fuzzy Logic with engineering applications”, McGraw-Hill, U.S.A., (1995).
  • [35] Dubey, M., Mastorakis, N.E., ”Tunning of Fuzzy Logic Power System Stabilizers using Genetic Algorithm in Multimachine Power System”, Wseas Transactions on Power Systems, 4(3): 105-114 (2009).
  • [36] McLean, D., “Automatic Flight Control Systems”, Prentice Hall, New York, U.S.A, (1990).
Yıl 2011, Cilt: 24 Sayı: 4, 781 - 789, 16.12.2011

Öz

Kaynakça

  • [1] Kahvecioglu, A., ”Flight control system design using multi-model approach and genetic algorithm”, Phd. Thesis, University of Anadolu, Eskisehir, (2000).
  • [2] Savran, A., Tasaltin, R., Becerikli, Y., “Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks”, ISA Transactions, 45(2): 225–247 (2006).
  • [3] Verbruggen, H.B., Zimmerman, H.J., Babuska, R. , “Fuzzy Algorithms for Control”, Kluwer Academic Publishers, Massachusetts, U.S.A, (1999).
  • [4] Robust Flight Control, A Design Challenge, Lecture Notes in Control and Information Sciences, Springer Berlin-Heidelberg, (224/1997).
  • [5] Aström, K.J., Wittenmark, B., “Adaptive control”, Addison Wesley, Lund Institute of Technology, U.S.A, (1989).
  • [6] Experience with the X-15 adaptive flight control system, http://www.nasa.gov/centers/dryden/pdf/87785mai n_H-618.pdf
  • [7] Enns, D.F., “Robustness of dynamic inversion vs. Mu synthesis: Lateral-directional flight control example”, Proceedings of the AIAA Conference on Guidance, Navigation, and Control, Portland, Oregon, (1990).
  • [8] Hayes, J.R., Bates, D.G., “LFT-based uncertainty modeling and mu-analysis of the HIRM+RIDE flight control law”, Proc. of the IEEE International Symposium on Computer Aided Control Systems Design (CACSD’02), Glasgow, 242–247, (2002).
  • [9] AI-Malki, M.F., Wei, G.D., “Advantages of using µ-synthesis for fault-tolerant flight control system”, IFAC Fault Detection, Supervision and Safety of Technical Processes, Beijing, Elsevier IFAC publication (2006).
  • [10] Nichols, R.A., Reichert R.T., “Gain Scheduling for H-Infinity Controllers: A Flight Control Example”, IEEE Transactions on Control Systems Technology, 1(2): 69-79 (1993).
  • [11] Kung, C.C., “Nonlinear H∞ robust control applied to F-16 aircraft with mass uncertainty using control surface inverse algorithm”, Journal of the Franklin Institute, 345: 851–876 (2008).
  • [12] Postlethwaitea, I., Prempain, E., Turkoglu, E., Turnera, M.C., Ellisb, K., Gubbelsb, A.W., “Design and flight testing of various H∞ controllers for the Bell 205 helicopter”, Control Engineering Practice, 13: 383–398, (2005).
  • [13] Ackermann, J., “Multi-Model Approaches to Robust Control System Design”, Lecture Notes in Control and Information Science, Springer Verlag ,70 (1985).
  • [14] Magni, J.F., “Multimodel eigenstructure assignment in flight control design”, Aerospace Science and Technology Elseiver Science, 3(3): 141-151 (1999).
  • [15] Li, Y., “Neuro-controller design for nonlinear Fighter aircraft maneuver using fully tuned RBF networks”, Automatica, 37: 1293-1301 (2001).
  • [16] Vijaya, K.M., “A direct adaptive neural command controller design for an unstable helicopter”, Engineering Applications of Artificial Intelligence, 22: 181–191 (2009).
  • [17] Savran, A., “Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks”, ISA Transactions, 45(2): 225–247 (2006).
  • [18] Wu, S.L., Chena, P.C., Hsu, C.H. , Chang, K.Y., “Gain-scheduled control of PVTOL aircraft dynamics with parameter-dependent disturbance”, Journal of the Franklin Institute, 345: 906–925 (2008).
  • [19] Yang, C.D., Luo, C.C, Shiu-Jeng, L., Chang, Y. H., “Applications of Genetic-Taguchi Algorithm in Flight Control Designs”, Journal of Aerospace Engineering, 18(4): 232-241 (2005).
  • [20] Fantinutto, R., Guglieri, G., Quagliotti, F.B., “Flight control system design and optimization with a genetic algorithm”, Aerospace Science and Technology, 9: 73–80 (2005).
  • [21] Jamshidi, M., Titli, A., Zadeh, L., Boverie, S., “Applications of fuzzy logic towards high machine intelligence quotient systems”, Prentice Hall PTR, New Jersey, (1997).
  • [22] Kantaa, A.F., Montavon, G., Planche, M.P., Coddet, C., “In-flight particle characteristics control by implementing a fuzzy logic controller”, Surface & Coatings Technology, 202: 4479–4482 (2008).
  • [23] Cordon, O., Gamide, F., Herrera, F. , Magdelen, H.L.,, “Ten Years of Genetic Fuzzy Systems Current Framework and New trends”, Fuzzy Sets and Systems, 141(1): 5-31 (2004).
  • [24] Sanchez, E., Shibata, T., Zadeh, L.A., “Genetic Algorithms and Fuzzy Logic Systems, Soft Computing Perspectives”, World Scientific, Singapore, (1997).
  • [25] Kim, M.S., “Fuzzy Controller Design with the Degree of Non-uniformity for the Scaled Active Steering Testbed in the Railway Vehicle”, Wseas Transactions on Systems and Control, 4(7): 306- 315 (2009).
  • [26] Popescu, M.C., Petrisor, A., Drighiciu, A., “Fuzzy Control of the Position for the Piston of an Industrial Robot”, 12th Wseas International Conference on Systems, Heraklion, Greece, 222- 226 (2008).
  • [27] Susnea, I., Vasiliu, G., Filipescu, A., ”Real-time, embedded fuzzy control of the Pioneer3-DX robo for path following”, 12th Wseas International Conference on Systems, Heraklion, Greece, 334- 338 (2008).
  • [28] Kiyak, E., “Flight control applications with fuzzy logic method”, MSc. Thesis, University of Anadolu, Eskisehir, (2003).
  • [29] Rotton, S.K., Brehm, T., Sandhu, G. S., “Analysis and Design of a Proportional Fuzzy Logic Controller”, Department of Electrical Engineering Wright State University, Dayton, Ohio (2005).
  • [30] Livchitz, M., Abershitz, A., Soudak, U., “Development of an automated fuzzy-logic- based expert system for an unmanned landing”, Fuzzy Sets and Systems, 93(2): 145-159, (1998).
  • [31] Beringer, D. B., Applying Performance-Controlled Systems, Fuzzy Logic, and Fly-By-Wire Controls to General Aviation, Civil Aerospace Medical Institute Federal Aviation Administration Oklahoma City, 2002, http://www.hf.faa.gov/docs/508/docs/cami/0207.pd f
  • [32] Işık, Y., Kahvecioğlu, A., Korul, H., ”Bir uçağın yunuslama açısı kontrolünde bulanık PD denetleyici performansının incelenmesi”, Kayseri VI. Havacılık Sempozyumu, Nevşehir, Türkiye, 434-438 (2006),
  • [33] Topuz, V., “Fuzzy genetic process control”, PhD. Thesis, University of Marmara, Istanbul, (2002). [34] Ross, T.J., “Fuzzy Logic with engineering applications”, McGraw-Hill, U.S.A., (1995).
  • [35] Dubey, M., Mastorakis, N.E., ”Tunning of Fuzzy Logic Power System Stabilizers using Genetic Algorithm in Multimachine Power System”, Wseas Transactions on Power Systems, 4(3): 105-114 (2009).
  • [36] McLean, D., “Automatic Flight Control Systems”, Prentice Hall, New York, U.S.A, (1990).
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Electrical & Electronics Engineering
Yazarlar

Yasemin Işık

Hakan Korul Bu kişi benim

Yayımlanma Tarihi 16 Aralık 2011
Yayımlandığı Sayı Yıl 2011 Cilt: 24 Sayı: 4

Kaynak Göster

APA Işık, Y., & Korul, H. (2011). Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System. Gazi University Journal of Science, 24(4), 781-789.
AMA Işık Y, Korul H. Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System. Gazi University Journal of Science. Aralık 2011;24(4):781-789.
Chicago Işık, Yasemin, ve Hakan Korul. “Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System”. Gazi University Journal of Science 24, sy. 4 (Aralık 2011): 781-89.
EndNote Işık Y, Korul H (01 Aralık 2011) Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System. Gazi University Journal of Science 24 4 781–789.
IEEE Y. Işık ve H. Korul, “Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System”, Gazi University Journal of Science, c. 24, sy. 4, ss. 781–789, 2011.
ISNAD Işık, Yasemin - Korul, Hakan. “Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System”. Gazi University Journal of Science 24/4 (Aralık 2011), 781-789.
JAMA Işık Y, Korul H. Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System. Gazi University Journal of Science. 2011;24:781–789.
MLA Işık, Yasemin ve Hakan Korul. “Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System”. Gazi University Journal of Science, c. 24, sy. 4, 2011, ss. 781-9.
Vancouver Işık Y, Korul H. Comparison of Classical PD and Fuzzy PD Controller Performances of an Aircraft Pitch Angle Control System. Gazi University Journal of Science. 2011;24(4):781-9.