北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (4): 650-661.doi: 10.13700/j.bh.1001-5965.2018.0463

• 论文 • 上一篇    下一篇

高超声速飞行器预设性能反演控制方法设计

李小兵1, 赵思源2, 卜祥伟1, 何阳光2   

  1. 1. 空军工程大学 防空反导学院, 西安 710051;
    2. 空军工程大学 研究生院, 西安 710051
  • 收稿日期:2018-07-31 出版日期:2019-04-20 发布日期:2019-04-26
  • 通讯作者: 李小兵 E-mail:1098547574@qq.com
  • 作者简介:李小兵,男,教授。主要研究方向:空天拦截器制导控制与仿真;赵思源,男,硕士研究生。主要研究方向:空天拦截器制导控制与仿真。
  • 基金资助:
    国家自然科学基金(61603410)

Design of prescribed performance backstepping control method for hypersonic flight vehicles

LI Xiaobing1, ZHAO Siyuan2, BU Xiangwei1, HE Yangguang2   

  1. 1. Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China;
    2. Graduate College, Air Force Engineering University, Xi'an 710051, China
  • Received:2018-07-31 Online:2019-04-20 Published:2019-04-26
  • Supported by:
    National Natural Science Foundation of China (61603410)

摘要: 为解决吸气式高超声速飞行器的飞行控制问题,提出了一种新型预设性能神经反演控制器设计方法。通过构造预设性能函数,保证速度跟踪误差和高度跟踪误差能够按照预设的收敛速度、超调量及稳态误差收敛至期望的区域,同时满足系统预设的瞬态性能和稳态精度。在反演控制设计结构下,引入径向基函数(RBF)神经网络对模型未知函数及不确定项进行逼近,提高了控制系统的鲁棒性。引入的RBF神经网络中仅有一个参数需要在线更新,有效提高了控制准确性,避免了通常反演控制方法中经常出现的"微分膨胀问题",并降低了计算量。通过仿真实验验证了所设计控制系统的有效性和可行性。

关键词: 高超声速飞行器, 预设性能, 反演控制, 瞬态性能, 径向基函数(RBF)神经网络

Abstract: In order to solve the flight control problem of the air-breathing hypersonic vehicle, a new design method of neural inversion controller with prescribed performance was proposed. By constructing a prescribed performance function, it is ensured that the velocity tracking error and the altitude tracking error can converge to a desired area according to the prescribed convergence speed, overshoot amount and steady state error, and satisfy the preset transient performance and steady state accuracy of the system. Under the backstepping control design structure, the radial basis function(RBF) neural network was introduced to approximate the model unknown function and uncertainties, which improved the robustness of the control system. Only one parameter of the introduced RBF neural network needed to be updated online, which effectively improved the control accuracy, avoided the "differential expansion problem" in the backstepping control method, and reduced the burden of calculation. Finally, the simulation experiments verify the effectiveness and feasibility of the designed control system.

Key words: hypersonic flight vehicles, prescribed performance, backstepping control, transient performance, radial basis function (RBF) neural network

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