Access the full text.
Sign up today, get DeepDyve free for 14 days.
Li Gao-yun (2010)
Robust LSSVM control for ship course-keeping systemControl and Decision
Jiang Chang-sheng (2008)
Improved robust and adaptive dynamic surface control for nonlinear systemsControl and Decision
Baohua Lian, H. Bang, J. Hurtado (2004)
Adaptive Backstepping Control Based Autopilot Design for Reentry Vehicle, 4
Li Tie-shan, Zou Zao-jian, Luo Wei-lin (2008)
DSC-backstepping Based Robust Adaptive NN Control for Nonlinear Systems
M. Krstić, P. Kokotovic, I. Kanellakopoulos (1995)
Nonlinear and adaptive control de-sign
S. Darbha, J. Hedrick, P. Yip, J. Gerdes (2000)
Dynamic surface control for a class of nonlinear systemsIEEE Trans. Autom. Control., 45
J. Suykens, L. Lukas, J. Vandewalle (2000)
Sparse approximation using least squares support vector machines2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2
S.F. Guo, G.Z. Shen, C.F. Wu
Advanced Flight Control System
L. Zhou
Robust adaptive control for nearspance vehicles based on backstepping approach
S. Tokat, S. Iplikci, Lutfi Ulusoy (2009)
Output feedback sliding mode control with support vector machine based observer gain adaptation
F. Shinskey, R. Rojas (2008)
2.19 Nonlinear and Adaptive Control
Tie-shan Li, Z. Zou, W. Luo (2009)
DSC-backstepping Based Robust Adaptive NN Control for Nonlinear Systems: DSC-backstepping Based Robust Adaptive NN Control for Nonlinear SystemsActa Automatica Sinica, 34
Dan Wang, Jie Huang (2005)
Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback formIEEE Transactions on Neural Networks, 16
Taeyoung Lee, Youdan Kim (2001)
Nonlinear Adaptive Flight Control Using Backstepping and Neural Networks ControllerJournal of Guidance Control and Dynamics, 24
(2001)
Sun is a Professor in the School of Automation at Southeast University, China. He received his MS and PhD degrees in Electrical Engineering from the Southeast University, Nanjing, China
J. Suykens (2001)
Support Vector Machines: A Nonlinear Modelling and Control PerspectiveEur. J. Control, 7
V. Vapnik (2000)
The Nature of Statistical Learning Theory
(2003)
Advanced Flight Control System, National Defence Industry Press, Beijing
S. Snell, D. Enns, W. Garrard (1992)
Nonlinear inversion flight control for a supermaneuverable aircraftJournal of Guidance Control and Dynamics, 15
Song Zhao-qing (2010)
Hypersonic Aircraft Dynamic Surface Adaptive Backstepping Control System Design Based on Uncertainty
Sahjendra Singh, M. Steinberg (1996)
Adaptive Control of Feedback Linearizable Nonlinear Systems With Application to Flight ControlJournal of Guidance Control and Dynamics, 19
J. Suykens (2001)
Nonlinear modelling and support vector machinesIMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188), 1
Manu Sharma, J. Farrell, M. Polycarpou, N. Richards, D. Ward (2003)
Backstepping Flight Control using On-Line Function Approximation
(2008)
Robust adaptive control for nearspance vehicles based on backstepping approach”, doctoral dissertation
J. Shaughnessy, S. Pinckney, J. Mcminn, C. Cruz, M. Kelley (1990)
Hypersonic vehicle simulation model: Winged-cone configuration
Athanassia Chalimourda, B. Scholkopf, Alex Smola (2004)
Experimentally optimal v in support vector regression for different noise models and parameter settingsNeural networks : the official journal of the International Neural Network Society, 18 2
Purpose – The purpose of this paper is to propose a robust control scheme for near space vehicle's (NSV's) reentry attitude tracking problem under aerodynamic parameter variations and external disturbances. Design/methodology/approach – The robust control scheme is composed of dynamic surface control (DSC) and least squares support vector machines (LS‐SVM). DSC is used to design a nonlinear controller for HSV; then, to increase the robustness and improve the control performance of the controller. LS‐SVM is presented to estimate the lumped uncertainties, including aerodynamic parameter variations and external disturbances. The stability analysis shows that all closed‐loop signals are bounded, with output tracking error and estimate error of LS‐SVM weights exponentially converging to small compacts. Findings – Simulation results demonstrate that the proposed method is effective, leading to promising performance. Originality/value – First, a robust control scheme composed of DSC and adaptive LS‐SVM is proposed for NSV's reentry attitude tracking problem under aerodynamic parameter variations and external disturbances; second, the proposed method can achieve more favorable tracking performances than conventional dynamic surface control because of employing LS‐SVM to estimate aerodynamic parameter variations and external disturbances.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Aug 17, 2012
Keywords: Dynamic surface control (DSC); Least squares support vector machine (LS‐SVM); Near space vehicle (NSV); Attitude control; Control technology; Control systems
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.