固定翼飞机动力学模型的子空间辨识方法

Subspace Model Identification Method for Flight Dynamics of Fixed-Wing Airplane

针对子空间模型辨识(SMI)方法这类线性模型辨识技术存在的建模误差,提出一种两阶段固定翼飞机飞行动力学模型辨识方法.首先采用基于辅助变量的闭环SMI方法辨识飞机的近似线性模型;然后,利用该模型构建扩张状态观测器,从而估计出系统中的非线性动态数据;在此基础上,进一步采用神经网络建立系统非线性动态的分散式模型.最后,利用B747飞机6自由度非线性模型进行系统辨识实验,所得结果验证了该方法的有效性.

Due to notable advantages of simplicity and high efficiency,the subspace model identification（SMI）method becomes an attractive modeling approach for flight dynamics of the airplane system.However,because of the nonlinearity in the airplane system,SMI based model identification approaches which are designed for linear systems always generate some modeling errors.To deal with this problem,a novel two-stage system identification approach for flight dynamics of the fixed-wing airplane system was proposed.An instrumental variable aided closed-loop SMI was first adopted to obtain the approximate linear model of the airplane dynamics.Then,an extended state observer（ESO）able to estimate the nonlinear dynamics of the airplane system was constructed using the obtained linear model.Based on the estimates by the ESO,agroup of neural networks were trained to identify the decentralized model of the system nonlinearity.Finally,the nonlinear,six degree-of-freedom（6DOF）model of a B747 airplane was applied to system identification test,and the results obtained verify the effectiveness of the proposed approach.