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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Air-Breathing Hypersonic Vehicles Funnel Control Using Neural Approximation of Non-affine Dynamics
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Air-Breathing Hypersonic Vehicles Funnel Control Using Neural Approximation of Non-affine Dynamics

机译：基于非仿射动力学的神经逼近的喘气超音速车辆漏斗控制

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摘要

This paper presents a funnel non-affine controller applying neural approximation for prescribed tracking of air-breathing hypersonic vehicles (AHVs). We propose a new funnel control to force velocity and altitude tracking errors to fall within bounded funnels, while the desired transient performance and steady-state performance are ensured for both tracking errors. To handle the non-affine dynamics, a simplified neural controller is addressed for a velocity subsystem based on implicit function theorem, and a new back-stepping control without virtual control laws is exploited for the altitude subsystem via a model transformation combined with low-pass-filter approach. Neural approximations and regulation laws for guaranteeing approximation performance are employed to reject system unknown dynamics. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov synthesis. Finally, the tracking performance of the proposed control approach is verified by simulation results.
机译：本文提出了一种应用神经逼近的漏斗非仿射控制器，用于空气超音速飞行器（AHV）的指定跟踪。我们提出了一种新的漏斗控制，以迫使速度和高度跟踪误差落入有界漏斗之内，同时确保了两种跟踪误差的理想瞬态性能和稳态性能。为了处理非仿射动力学，基于隐函数定理为速度子系统提供了简化的神经控制器，并通过模型转换与低通相结合，为高度子系统开发了一种新的无虚拟控制律的后退控制-filter方法。采用神经逼近和调节律来保证逼近性能，以拒绝系统未知的动力学。通过Lyapunov综合可以保证所有闭环系统信号的半全局一致极限极限。最后，仿真结果验证了该控制方法的跟踪性能。

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