Follow-up study of aircraft parameter estimation for lateral dynamics using self-adaptive teaching-learning based optimization with acceptance probability

Abstract

This paper is a follow-up on using self-adaptive teaching-learning based optimization with an acceptance probability (SATLBO-AP), tailored specifically for aircraft parameter estimation, and extended to aircraft lateral dynamics, previously tested for aircraft longitudinal dynamics. The lateral dynamic is more complicated than the longitudinal with additional parameters, input and output. Since the problem has changed, the performance of SATLABO-AP requires reevaluation. Thus, a comparison between newly developed algorithms and recently proposed algorithms is conducted. The problem setup is carried out in a way similar to earlier work, but with a lateral dynamic. The results show that SATLBO-AP outperforms other algorithms in terms of convergence and consistency regarding noise level added to the validation signal.