Neural control of hypersonic flight vehicle model via time-scale decomposition with throttle setting constraint
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O R I G I N A L PA P E R
Neural control of hypersonic flight vehicle model via time-scale decomposition with throttle setting constraint Bin Xu · Zhongke Shi · Chenguang Yang · Shixing Wang
Received: 20 November 2012 / Accepted: 11 April 2013 / Published online: 7 May 2013 © Springer Science+Business Media Dordrecht 2013
Abstract Considering the use of digital computers and samplers in the control circuitry, this paper describes the controller design in discrete time for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV) with Neural Network (NN). Motivated by time-scale decomposition, the states are decomposed into slow dynamics of velocity, altitude and fast dynamics of attitude angles. By command transformation, the reference command for γ − θp − q subsystem is derived from h − γ subsystem. Furthermore, to simplify the backstepping design, we propose the controller for γ − θp − q subsystem from prediction function without virtual controller. For the velocity subsystem, the throttle setting constraint is considered and new NN adaption law is designed by auxiliary error dynamics. The uniformly ultimately boundedness (UUB) of the system is proved by Lyapunov stability method. Simulation results show the effectiveness of the proposed algorithm. B. Xu () · Z. Shi School of Automation, Northwestern Polytechnical University, Xi’an, China e-mail: [email protected] C. Yang School of Computing and Mathematics, Plymouth University, Plymouth, UK S. Wang Department of Control Engineering, Naval Aeronautical and Astronautical University, Yantai, China
Keywords Hypersonic flight control · Time-scale decomposition · System transformation · Neural network saturation
1 Introduction Hypersonic flight vehicles are intended to present a reliable and more cost-efficient way to access space. As the key issue of making hypersonic vehicles feasible, hypersonic flight control is very important [6]. Different ideas have been applied on the control of the dynamics such as H∞ design and μ-synthesis method [2], and sliding mode control [23]. Due to the approximation ability of fuzzy system [7, 16] and NN [4, 26], intelligent control becomes an important topic for flight control [5]. With further vision of the cascade form of hypersonic flight dynamics, dynamic surface controller design [9, 10, 19] is proposed based on the decomposition of the equations into functional subsystems. In [1], with time-scale decomposition, the altitude and velocity are considered as slow dynamics while the attitude-related states are fast dynamics. As pointed out in [13, 25], the use of digital computers and samplers in the control circuitry has made the use of discrete-time system representation more straightforward for controller design than continuoustime representation. For the control of flight vehicle, controller on the basis of continuous system is usually implemented by a digital computer with a certain sampling interval [3, 17]. In [15], it is illustrated that
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