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高超声速飞行器由于具有特殊的气动特性和复杂的运行环境,其气动模型的建立和模型中参数的确定面临着更高的要求.飞行器参数辨识是根据飞行器的输入及其响应确定出飞行器的模型和模型中的各个参数数值.针对高超声速飞行器模型耦合性强、非线性程度高、运行环境复杂等特点,本文提出了基于人工蜂群优化的在线参数辨识方法,将参数辨识问题转换为优化问题,以蜂群为单位进行搜索,通过群体信息交流和优胜劣汰的机制,使得蜂群朝着更优方向进化;引入采蜜蜂机制和混沌搜索机制,使得蜂群能够跳出局部最优,具有更强的全局寻优能力.应用此方法对某飞行器升力系数进行辨识计算,结果证明了此方法的可行性.与传统的极大似然法对比表明,本文所提方法在具有系统测量噪声的条件下具有更强的抗干扰能力和准确性.
Due to its special aerodynamic characteristics and complex operating environment, the hypersonic vehicle faces higher requirements for the establishment of the aerodynamic model and the determination of parameters in the model.Aircraft parameter identification is based on the aircraft's input and its response to determine the model of the aircraft and In this paper, an online parameter identification method based on artificial bee colony optimization is proposed in this paper, which converts the parameter identification problem into an optimization problem, which is based on the characteristics of the hypersonic vehicle model such as strong coupling, high nonlinearity and complex operating environment. In order to search for the bee colony, through the exchange of group information and the mechanism of survival of the fittest, the bee population evolves toward a better direction. The mechanism of collecting bees and the chaos search mechanism are introduced to make the colony jump out of the local optimum and have stronger global Search for optimal ability.According to this method, the lift coefficient of an aircraft is identified and calculated, and the result proves the feasibility of this method.Comparing with the traditional maximum likelihood method, this method shows that the proposed method has more advantages Strong anti-interference ability and accuracy.