Nonlinear identification of aircraft

In flight dynamics and control, a growing interest in identification is induced by the fact that identification allows one to find the dynamical models for analysis and optimization. Identification plays a key role in nonlinear analysis and control as well as in diagnostics and design of self-repairing reconfigurable algorithms for advanced vehicles. This paper treats the parameter identification issues for nonlinear multivariable aircraft models and reports results in the convergence analysis of the proposed identification algorithm. To handle the nonlinear identification, the model-based framework and Lyapunov's concept are applied. The second method establishes and unifies analytical results in stability. Using Lyapunov's concept, the conditions which ensure the convergence of the parameters are given. The explored identification algorithm has significant advantages for analysis, control and diagnostics of nonlinear continuous-time systems. The unknown parameters of nonlinear aircraft models can be obtained using the developed innovative procedure in the straightforward way. The proposed technique is computationally efficient and the identification can be accomplished on-line in real-time. To support the and to illustrate the practical the methodology, longitudinal derivatives for a high performance aircraft are found employing the available flight data. The modeling results are compared to the experimental data.