Maximum likelihood method based on interior point algorithm for aircraft parameter identification

An alternative method is proposed to identify unknown parameters of a nonlinear system with some information available, which uses the interior point algorithm as an optimizer and ensures that the identification algorithm always give a reasonable result. The proposed identification method provides a promising way to obtain the true value of unknown parameters. To verify the maximum likelihood method based on interior point algorithm with variable constraints, an example of identifying longitudinal derivatives is considered based on simulated flight data. It is observed that the maximum likelihood based on the interior point algorithm provides a smoother variation of the identification results and requires fewer iterations for convergence.