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

Kanokmedhakul Y., Panagant N., Bureerat S., Pholdee N., YILDIZ A. R.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, cilt.2021, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 2021
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1155/2021/4740995
  • Dergi Adı: COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, EMBASE, INSPEC, MEDLINE, Metadex, Psycinfo, Directory of Open Access Journals, Civil Engineering Abstracts
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

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.