Aircraft longitudinal stability and control derivatives identification by using life cycle and Levenberg-Marquardt optimization algorithms

Resumo

In this work, two optimization algorithms are investigated to accomplish the parameter identification of the longitudinal motion of a real aircraft by using the output error method. The first algorithm is the nature-inspired algorithm named the life cycle model, which is a composed strategy based on other heuristics such as genetic algorithms and particle swarm optimization. The second one is the gradient-based technique named Levenberg-Marquardt algorithm, which is a variant of the Gauss-Newton method. Flight test data, performed with a training jet aircraft (Xavante AT-26), were used to feed the output error method. In this context, both optimization algorithms were tested, in solo performance and in a cascade-type approach. Results are reported, aiming to illustrate the success of using the proposed methodology.

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