Aircraft Control Parameter Estimation Using Self-Adaptive Teaching-Learning-Based Optimization with an Acceptance Probability

Yodsadej Kanokmedhakul; Natee Panagant; Sujin Bureerat; Nantiwat Pholdee; Ali R Yildiz

doi:10.1155/2021/4740995

Aircraft Control Parameter Estimation Using Self-Adaptive Teaching-Learning-Based Optimization with an Acceptance Probability

Comput Intell Neurosci. 2021 Dec 20:2021:4740995. doi: 10.1155/2021/4740995. eCollection 2021.

Authors

Yodsadej Kanokmedhakul¹, Natee Panagant¹, Sujin Bureerat¹, Nantiwat Pholdee¹, Ali R Yildiz²

Affiliations

¹ Sustainable Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand.
² Department of Mechanical Engineering, Bursa Uludag University, Bursa, Turkey.

Abstract

This work presents a metaheuristic (MH) termed, self-adaptive teaching-learning-based optimization, with an acceptance probability for aircraft parameter estimation. An inverse optimization problem is presented for aircraft longitudinal parameter estimation. The problem is posed to find longitudinal aerodynamic parameters by minimising errors between real flight data and those calculated from the dynamic equations. The HANSA-3 aircraft is used for numerical validation. Several established MHs along with the proposed algorithm are used to solve the proposed optimization problem, while their search performance is investigated compared to a conventional output error method (OEM). The results show that the proposed algorithm is the best performer in terms of search convergence and consistency. This work is said to be the baseline for purely applying MHs for aircraft parameter estimation.

MeSH terms

Aircraft*
Algorithms*
Learning
Probability