Online from: 2008
Subject Area: Electrical & Electronic Engineering
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|Title:||On-line RNN compensated second order nonsingular terminal sliding mode control for hypersonic vehicle|
|Author(s):||Ruimin Zhang, (School of Automation, Southeast University, Nanjing, China and School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China), Li Wang, (School of Automation, Southeast University, Nanjing, China), Yingjiang Zhou, (School of Automation, Southeast University, Nanjing, China)|
|Citation:||Ruimin Zhang, Li Wang, Yingjiang Zhou, (2012) "On-line RNN compensated second order nonsingular terminal sliding mode control for hypersonic vehicle", International Journal of Intelligent Computing and Cybernetics, Vol. 5 Iss: 2, pp.186 - 205|
|Keywords:||Hypersonic flow, Hypersonic vehicle, Neural nets, Nonsingular terminal sliding mode control, Recurrent neural network, Second order sliding mode control, Super twisting control algorithm|
|Article type:||Research paper|
|DOI:||10.1108/17563781211231534 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||This work is supported by the National Outstanding Youth Science Foundation (61125306) and the National Natural Science Foundation of Major Research Plan (91016004).|
Purpose – The purpose of this paper is to design a robust control scheme to achieve robust tracking of velocity and altitude commands for a general hypersonic vehicle (HSV) in the presence of parameter variations and external disturbances.
Design/methodology/approach – The robust control scheme is composed of nonsingular terminal sliding mode control (NTSMC), super twisting control algorithm (STC) and recurrent neural network (RNN). First, by combing a novel NTSMC and STC algorithm, a second order NTSMC approach for HSV is proposed to provide fast, continuous and high precision tracking control. Second to relax the requirements for the bounds of the lumped uncertainties in control design, a RNN disturbance observer is presented to increase the robustness of the control system. The weights of RNN are updated by adaptive laws based on Lyapunov theorem, thus the closed-loop stability can be guaranteed.
Findings – Simulation results demonstrate that the proposed method is effective, leading to promising performance.
Originality/value – The main contributions of this work are: first, both parameter variations and external disturbances are considered in control design for the longitudinal dynamic model of HSV; and second, the proposed controller can remove chattering and achieve more favorable tracking performances than conventional sliding mode control.
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