基于离散时间神经网络方法的高超声速飞行器跟踪控制OA
Tracking control of hypersonic flight vehicle based on discrete-time neural network approach
高超声速飞行器是一个具有强非线性、强耦合等特性的多输入多输出系统.此外,当升降舵作为唯一控制舵面时,系统模型中的升降舵-升力耦合项会导致高超声速飞行器系统呈现非最小相位特性,因此其跟踪控制问题具有一定的挑战性.本文基于离散时间输出调节理论,研究了升降舵作为唯一控制舵面条件下高超声速飞行器的跟踪控制问题.首先将高超声速飞行器的跟踪控制问题描述为近似离散时间输出调节问题.由于高超声速飞行器对应的离散调节器方程的精确解不可得,本文通过神经网络方法来获取离散调节器方程的近似解,进而设计一个离散时间神经网络控制器来实现高超声速飞行器的跟踪控制.仿真结果表明,本文所提出的控制算法具有良好的跟踪性能.
Hypersonic flight vehicle is a multi-input multi-output system with strong nonlinearity and strong coupling characteristics.In addition,when the elevator is used as the only control surface,the coupling term between the elevator and lift force in the system model leads to the non-minimum phase behavior of the hypersonic flight vehicle system,which poses certain challenge to its tracking control problem.This paper investigates the tracking control problem of hypersonic flight vehicle based on discrete-time output regulation theory,where the elevator is the only control surface.The tracking control problem of hypersonic flight vehicle is firstly formulated as an approximate discrete-time output regulation problem.Since it is difficult to obtain the exact solution of the discrete regulator equations for the hypersonic flight vehicle,the approximate solution of the discrete regulator equations is obtained by neural network method and then the discrete-time neural network controller is designed to achieve tracking control of hypersonic flight vehicle in this paper.The simulation results show that the proposed control algorithm can lead to satisfactory tracking performance.
何声瑞;平兆武;张宏伟
合肥工业大学电气与自动化工程学院,安徽 合肥 230009工业自动化安徽省工程技术研究中心,安徽 合肥 230009合肥工业大学电气与自动化工程学院,安徽 合肥 230009
离散时间非线性系统高超声速飞行器跟踪控制输出调节神经网络
discrete-time nonlinear systemhypersonic flight vehicletracking controloutput regulationneural net-work
《控制理论与应用》 2026 (1)
52-60,9
国家自然科学基金项目(62273127,62473114),中央高校基本科研业务费项目(JZ2025HGTG0293),深圳市科技计划项目(JCYJ2022081810241603 6)资助.Supported by the National Natural Science Foundation of China(62273127,62473114),the Fundamental Research Funds for the Central Universities(JZ2025HGTG0293)and the Shenzhen Science and Technology Program(JCYJ20220818102416036).
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