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.
Highlights
Air-breathing hypersonic vehicles (AHVs) have been viewed as a critical solution to achieving reliable affordable access to near-space for both commercial and military applications.[1,2,3] the special peculiarities of AHVs’ dynamic characteristics and aerodynamic effects make the control design highly challenging.[4,5] the motion model constructed for AHVs must be highly nonlinear and coupled owing to the airframe-integrated scramjet engines and time-varying flight conditions
Flight control designs for AHVs have been given special considerations, and various control methodologies have been addressed based on simplified affine models of AHVs instead of nonaffine ones
A novel prescribed performance control approach using nonaffine models is proposed for an AHV based on neural approximation
Summary
Air-breathing hypersonic vehicles (AHVs) have been viewed as a critical solution to achieving reliable affordable access to near-space for both commercial and military applications.[1,2,3] the special peculiarities of AHVs’ dynamic characteristics and aerodynamic effects make the control design highly challenging.[4,5] the motion model constructed for AHVs must be highly nonlinear and coupled owing to the airframe-integrated scramjet engines and time-varying flight conditions. A novel prescribed performance control approach using nonaffine models is proposed for an AHV based on neural approximation. 2. Different from traditional neural control strategies,[19,21] the system uncertainties are lumped together and advanced regulation schemes are developed to directly estimate the norm of neural networks, on the basis of which both the required neural networks and online learning parameters are reduced greatly, yielding a low-computational burden design. 3. By comparison with traditional prescribed performance control approaches,[28,29] better transient performance guaranteeing small overshoot can be imposed on tracking errors based on a newly constructed prescribed performance mechanism. Bounds such that the desired transient performance and steady-state performance are guaranteed By prescribed performance, it denotes that the tracking error e(t) is strictly limited within an arbitrarily small residual set. If eðtÞ is bounded, the tracking error eðtÞ can be limited to the constructed prescribed behavior bound (10)
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