Adaptive Dynamic Surface Control of a Supersonic Flight Vehicle

Waseem Aslam Butt; Lin Yan; Amezquita S. Kendrick

doi:10.4028/www.scientific.net/AMM.110-116.3580

Paper Titles

Data Acquisition and Preparation Methods for Continuously Cast Billets Quality Analysis Software
p.3557

Experimental Investigations of Cutting Conditions Influence on Main Cutting Force and Surface Roughness Based on Chip Form Types in Cast Nylon Turning
p.3563

Direct Metal Deposition of Stainless Steel Wire Using Metal Inert Gas as Heat Source for Repair Purposes
p.3570

Optimization of GMAW Process Using Imperialist Competitive Algorithm
p.3575

Adaptive Dynamic Surface Control of a Supersonic Flight Vehicle
p.3580

Optimal Control for Combined Energy Storage and Attitude Control System (CEACS) in Small Satellites
p.3587

Nonlinear Dynamics of Rotors due to Large Deformations and Shear Effects
p.3593

Recent Advances in Ultraprecision Diamond Turning of Non-Rotationally Symmetric Optical Surfaces
p.3600

Forced Vibration Behavior of Adhesively Bonded Single-Lap Joint
p.3611

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 110-116Adaptive Dynamic Surface Control of a Supersonic...

Adaptive Dynamic Surface Control of a Supersonic Flight Vehicle

Abstract:

The design of a nonlinear adaptive dynamic surface controller for the longitudinal model of a hypothetical supersonic flight vehicle is considered in this work. The uncertain nonlinear functions in the strict feedback flight vehicle model are approximated by using radial basis function neural networks. A detailed stability analysis of the designed angle-of-attack controller shows that all the signals of the closed loop system are uniformly ultimately bounded. The performance of the designed controller is verified through numerical simulations of the flight vehicle model.

You might also be interested in these eBooks

Mechanical and Aerospace Engineering, ICMAE2011

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 110-116)

Pages:

3580-3586

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.110-116.3580

Citation:

Cite this paper

Online since:

October 2011

Authors:

Waseem Aslam Butt, Lin Yan, Amezquita S. Kendrick

Keywords:

Adaptive Control, Dynamic Surface Control (DSC), Neural Network (NN), Supersonic Flight Vehicle

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] R. A. Nicholas, R. T. Richert, and W. J. Rugh, Gain Scheduling for H_Infinity Controllers: A Flight Control Example, IEEE Trans. On Control System Technology, vol. 1, 1993, pp.69-79.

DOI: 10.1109/87.238400

Google Scholar

[2] M. Bahrami, B. Ebrahimi, and G.R. Ansarifar, Sliding Mode Observer and Control Design with Adaptive Parameter Estimation for a Supersonic Flight Vehicle, International Journal of Aerospace Engineering, 2010, doi: 10. 1155/2010/474537.

DOI: 10.1155/2010/474537

Google Scholar

[3] L.I. De-fu and F.A. Jun-fang, Missile Autopilot Design Using Backstepping Approach, Proc. Second International Conference on Computer and Electrical Engineering, 2009, pp.238-241.

DOI: 10.1109/iccee.2009.51

Google Scholar

[4] M. Krstic, I. Kanellakopoulos, and P. V. Kokotovic. Nonlinear and Adaptive Control Design. New York: Wiley, (1995).

Google Scholar

[5] I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse. Systematic Design of Adaptive Controllers for Feedback Linearizable Systems, IEEE Transactions on Automatic Control, vol. 36, no. 11, 1991, pp.1241-1253. doi: 10. 1109/9. 100933.

DOI: 10.1109/9.100933

Google Scholar

[6] D. Swaroop, J. K. Hedrick, P. P. Yip, and J. C. Gerdes. Dynamic Surface Control for a Class of Nonlinear Systems., IEEE Transactions on Automatic Control, vol. 45, no. 10, 2000, pp.1893-1899. doi: 10. 1109/TAC. 2000. 880994.

DOI: 10.1109/tac.2000.880994

Google Scholar

[7] S. Bongsob, A. Howell and J. K. Hedrick. Dynamic Surface Control design for a Class of Non-linear Systems, Proc. 40th IEEE Conference on Decision and Control, Orlando, Florida, 2001, pp.2797-2802. doi: 10. 1109/2001. 980697.

Google Scholar

[8] J. Park and I. W. Sandberg, Universal Approximation Using Radial-Basis Function Networks, Neural Computat, vol. 3, 1991, pp.246-257. doi: 10. 1162/neco. 1991. 3. 2. 246.

DOI: 10.1162/neco.1991.3.2.246

Google Scholar