An aircraft´s parameter identification algorithm based on cloud model optimization

The maximum likelihood (ML) estimation method has been extensively applied to identifying the parameters of an aircraft. But it has to derive sensitivity equations in advance and solve sensitivity matrices, thus being complicated for its application and easily reaching locally optimal solutions. The paper proposes an aircraft´s parameter identification algorithm, which optimizes the ML function with the cloud model optimization theory in accordance with the ML estimation principle, thus obtaining the values of the parameters to be identified. The algorithm does not have to derive sensitivity matrices, has no high requirements for initial values and is little affected by noise. Thus it is easy to apply, can be optimized by the cloud model and have rather fast convergence and nice global search capability and thus not easily reaching locally optimal solutions. The Twin Otter airplane is used as a numerical example to verify the algorithm. The numerical results show that the parameter identification algorithm is easy to implement, has good identification precision and fast convergence and does not reach locally optimal solutions.