Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance. (30th January 2018)

• Record Type: Journal Article
• Authors: Bu, Xiangwei; Wang, Qing
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 1(2018:Jan./Feb.)
• Publication Date: 2018-01-30
• Subjects: Air-breathing hypersonic vehicle -- neural network -- nonaffine control -- prescribed performance; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418755246
• Languages: English
• ISSNs: 1729-8806

Abstract

This article investigates a novel nonaffine control strategy using neural networks for an air-breathing hypersonic vehicle. Actual actuators are regarded as additional state variables and virtual control inputs are derived from low-computational cost neural approximations, while a new altitude control design independent of affine models is addressed for air-breathing hypersonic vehicles. To further reduce the computational load, an advanced regulation algorithm is applied to devise adaptive laws for neural estimations. Moreover, a new prescribed performance mechanism is exploited, which imposes preselected bounds on the transient and steady-state tracking error performance via developing new performance functions, capable of guaranteeing altitude and velocity tracking errors with small overshoots. Unlike some existing neural control methodologies, the proposed prescribed performance-based nonaffine control approach can ensure tracking errors with preselected transient and steady-state performance. Meanwhile, the complex design procedure of backstepping is also avoided. Finally, simulation results are presented to validate the design.