{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:34:06Z","timestamp":1761896046936},"reference-count":38,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,7,1]]},"abstract":"<p>The complexity of helicopter flight dynamics makes modeling and helicopter system identification a very difficult task. Most of the traditional techniques require a model structure to be defined a priori and in case of helicopter dynamics, this is difficult due to its complexity and the interplay between various subsystems. To overcome this difficulty, non-parametric approaches are commonly adopted for helicopter system identification. Artificial Neural Network are a widely used class of algorithms for non-parametric system identification, among them, the Nonlinear Auto Regressive eXogeneous input network (NARX) model is very popular, but it also necessitates some in-depth knowledge regarding the system being modelled. There have been many approaches proposed to circumvent this and yet still retain the advantageous characteristics. In this paper, the authors carry out an extensive study of one such newly proposed approach - using a modified NARX model with a II-tiered, externally driven recurrent neural network architecture. This is coupled with an outer optimization routine for evolving the order of the system. This generic architecture is comprehensively explored to ascertain its usability and critically asses its potential. Different implementations of this architecture, based on nature inspired techniques, namely, Artificial Bee Colony (ABC), Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) are evaluated and critically compared in this paper. Simulations have been carried out for identifying the longitudinally uncoupled dynamics. Results of identification indicate a quite close correlation between the actual and the predicted response of the helicopter for all the models.<\/p>","DOI":"10.4018\/ijamc.2015070102","type":"journal-article","created":{"date-parts":[[2015,5,18]],"date-time":"2015-05-18T11:53:21Z","timestamp":1431950001000},"page":"38-52","source":"Crossref","is-referenced-by-count":4,"title":["Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques"],"prefix":"10.4018","volume":"6","author":[{"given":"S. N.","family":"Omkar","sequence":"first","affiliation":[{"name":"Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India"}]},{"given":"Dheevatsa","family":"Mudigere","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India"}]},{"given":"J.","family":"Senthilnath","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India"}]},{"given":"M. Vijaya","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India"}]}],"member":"2432","reference":[{"key":"ijamc.2015070102-0","unstructured":"Anon (1991). Rotorcraft System Identification. AGARD-AR-280."},{"key":"ijamc.2015070102-1","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(71)90059-8"},{"key":"ijamc.2015070102-2","doi-asserted-by":"publisher","DOI":"10.1162\/evco.1993.1.1.1"},{"key":"ijamc.2015070102-3","doi-asserted-by":"publisher","DOI":"10.1080\/00207179008934126"},{"key":"ijamc.2015070102-4","first-page":"212","article-title":"An Approach to Solve Multiobjective Optimization Problems Based on an Artificial Immune System.","volume":"2002","author":"C. A.Coello Coello","year":"2002","journal-title":"ICARIS"},{"key":"ijamc.2015070102-5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.07.022"},{"key":"ijamc.2015070102-6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-59901-9"},{"key":"ijamc.2015070102-7","doi-asserted-by":"crossref","unstructured":"De Castro, L. N., & Timmis, J. (2002). An artificial immune network for multimodal function optimization. In: Proceedings of CEC 2002: IEEE Congress on Evolutionary Computation, pp. 699\u2013704.","DOI":"10.1109\/CEC.2002.1007011"},{"key":"ijamc.2015070102-8","unstructured":"De Castro, L. N., & Von Zuben, F. J. (2000). The clonal selection algorithm with engineering applications, In: Workshop on Artificial Immune Systems and their Applications. GECCO, pp. 36\u201337."},{"key":"ijamc.2015070102-9","article-title":"A new optimizer using particle swarm theory","author":"R.Eberchart","year":"1995","journal-title":"Proc"},{"key":"ijamc.2015070102-10","unstructured":"Eberchart, R., & Kennedy, J. (1995). Particle swarm optimization. In Proc.of IEEE Int. Conf. Neural Networks, Perth, Australia."},{"key":"ijamc.2015070102-11","first-page":"1","article-title":"Training product unit neural networks.","volume":"2","author":"P.Engelbrecht","year":"1999","journal-title":"Stability Control: Theory Appl."},{"key":"ijamc.2015070102-12","first-page":"320","volume":"Vol. 496","author":"S.Forrest","year":"1991","journal-title":"Genetic Algorithms and the Immune System\u2014Parallel Problem Solving from Nature"},{"key":"ijamc.2015070102-13","unstructured":"Fu, K. H., & Kaletka, J. (1990). Frequency-Domain Identification of BO 105: Derivatives Models with Rotor Degrees of Freedom. 16th European Rotorcraft Forum, Glasgow, United Kingdom."},{"key":"ijamc.2015070102-14","unstructured":"Fu, K. H., & Marchand, M. (1983). Helicopter System Identification in the Frequency Domain. 9th European Rotorcraft Forum, Stresa, Italy."},{"key":"ijamc.2015070102-15","author":"D. E.Goldberg","year":"1989","journal-title":"Genetic Algorithms in Search Optimization and Machine Learning"},{"key":"ijamc.2015070102-16","author":"S.Haykin","year":"1994","journal-title":"Neural Networks \u2013 A Comprehensive Foundation"},{"key":"ijamc.2015070102-17","doi-asserted-by":"publisher","DOI":"10.1109\/72.125870"},{"key":"ijamc.2015070102-18","doi-asserted-by":"publisher","DOI":"10.1109\/72.125863"},{"key":"ijamc.2015070102-19","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4684-7662-0"},{"key":"ijamc.2015070102-20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73729-2_30"},{"key":"ijamc.2015070102-21","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2007.05.007"},{"key":"ijamc.2015070102-22","author":"B.Kuzta","year":"1983","journal-title":"Modeling and Identification of Dynamic Systems"},{"key":"ijamc.2015070102-23","unstructured":"Maine, R. E., & Iliff, K. W. (1985). Identification of dynamic system; theory and formulation. NASA RP-1138."},{"key":"ijamc.2015070102-24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2004.07.034"},{"key":"ijamc.2015070102-25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-97239-3"},{"key":"ijamc.2015070102-26","unstructured":"Mudigere, D., Omkar, S. N., & Vijay Kumar, M. (2008). Identification of helicopter dynamics using a PSO based approach. In proc of 64th Annual Forum and Technology Display, American Helicopter Society, Montreal, Canada."},{"key":"ijamc.2015070102-27","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.1989.70448"},{"key":"ijamc.2015070102-28","doi-asserted-by":"publisher","DOI":"10.1109\/72.80202"},{"key":"ijamc.2015070102-29","doi-asserted-by":"publisher","DOI":"10.1109\/72.80336"},{"key":"ijamc.2015070102-30","doi-asserted-by":"publisher","DOI":"10.4018\/jamc.2011070101"},{"key":"ijamc.2015070102-31","doi-asserted-by":"publisher","DOI":"10.1109\/72.279193"},{"key":"ijamc.2015070102-32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2012.07.003"},{"key":"ijamc.2015070102-33","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2014.03.070"},{"key":"ijamc.2015070102-34","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2012.2230432"},{"key":"ijamc.2015070102-35","first-page":"35","author":"M. B.Tischler","year":"1996","journal-title":"System Identification Methods for Aircraft Flight Control Development and Validation"},{"issue":"3","key":"ijamc.2015070102-36","first-page":"3","article-title":"Frequency-Response Method for Rotorcraft System Identification: Flight Applications to BO 105 Coupled Rotor\/Fuselage Dynamics.","volume":"37","author":"M. B.Tischler","year":"1992","journal-title":"Journal of the American Helicopter Society"},{"key":"ijamc.2015070102-37","unstructured":"Vijaya Kumar, M., & Omkar, S. N., Ranjan Ganguli, Prasad Sampath, & Suresh S., (2003). Identification of Helicopter Dynamics using Recurrent Neural Networks and Flight Data. In proc of 59th Annual Forum of the American Helicopter Society, Phoenix, Arizona, USA."}],"container-title":["International Journal of Applied Metaheuristic Computing"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=129010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T19:54:02Z","timestamp":1654113242000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/ijamc.2015070102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2015,7,1]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2015,7]]}},"URL":"https:\/\/doi.org\/10.4018\/ijamc.2015070102","relation":{},"ISSN":["1947-8283","1947-8291"],"issn-type":[{"value":"1947-8283","type":"print"},{"value":"1947-8291","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,7,1]]}}}