{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:55:20Z","timestamp":1754157320715,"version":"3.41.2"},"reference-count":17,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2013,3,22]],"date-time":"2013-03-22T00:00:00Z","timestamp":1363910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,3,22]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to build nonlinear model of a small rotorcraft\u2010based unmanned aerial vehicles (RUAV), using nonlinear system identification method to estimate the parameters of the model. The nonlinear model will be used in robust control system design and aerodynamic analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The nonlinear model is built based on mechanism theory, aerodynamics and mechanics, which can reflect most dynamics in large flight envelop. Genetic algorithm (GA) and time domain flight data is adopted to estimate unknown parameters of the model. The flight data were collected from a series of fight tests. The identification results were also analyzed and validated.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The nonlinear model of RUAV has better accuracy, the parameters are physical quantities, and having distinctly recognizable values. The GA is suitable for nonlinear system identification. And the results proved the identified model can reflect the dynamic characteristics in extensive area of flight envelop.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>The GA requires much more computing power, to identify 12 unknown parameters with 30 iterations, will takes more than 18 hours of a four cores desktop computer. Because of this is an off\u2010line identification process, and has more accuracy, extra time is acceptable.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>GA method has significantly increased the accuracy of the model. The previous work of system identification used a ten states linear model, and using PEM identified 23 coefficients. By carefully building the nonlinear model, it has only 21 unknown parameters, but if the model is linearized, it will get a linear model more than 35 states, which shows nonlinear model contain more dynamics than linear model.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17563781311301517","type":"journal-article","created":{"date-parts":[[2013,3,14]],"date-time":"2013-03-14T15:48:25Z","timestamp":1363276105000},"page":"45-61","source":"Crossref","is-referenced-by-count":8,"title":["Nonlinear system modeling and identification of small helicopter based on genetic algorithm"],"prefix":"10.1108","volume":"6","author":[{"given":"Fan","family":"Yang","sequence":"first","affiliation":[]},{"given":"Zongji","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Wei","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022021620472233500_b13","doi-asserted-by":"crossref","unstructured":"Bendotti, P. and Morris, J.C. (1995), \u201cRobust hover control for a model helicopter\u201d, American Control Conference, Vol. 1, pp. 682\u20107.","DOI":"10.1109\/ACC.1995.529337"},{"key":"key2022021620472233500_b10","unstructured":"Bernard, M. (2001), \u201cModeling small\u2010scale unmanned rotor\u2010craft for advanced flight control design\u201d, Dissertation Abstracts International, Vol. 62\u201001, p. 198."},{"key":"key2022021620472233500_b12","unstructured":"Bernard, M. and Kanade, T. (2000), \u201cSystem identification modeling of a model\u2010scale helicopter\u201d, Technical Report, CMU\u2010RI\u2010TR\u201000\u201003, Carnegie Mellon University, The Robotics Institute, Pittsburgh, PA, pp. 1\u201025."},{"key":"key2022021620472233500_b9","doi-asserted-by":"crossref","unstructured":"Bernard, M., Tischler, M. and Kanade, T. (2002), \u201cSystem identification of small\u2010size unmanned helicopter dynamics\u201d, Journal of the American Helicopter Society, Vol. 47 No. 1, pp. 50\u201063.","DOI":"10.4050\/JAHS.47.50"},{"key":"key2022021620472233500_b11","unstructured":"Civita, M.L., Messner, W.C. and Kanade, T. (2002), \u201cModeling of small\u2010scale helicopters with integrated first\u2010principles and system identification techniques\u201d, The American Helicopter Society 58th Annual Forum, Montreal, Canada, pp. 1426\u201037."},{"key":"key2022021620472233500_b3","unstructured":"del Cerro, J., Valero, J. and Barrientos, A. (2004), \u201cModelling and identification of a small unmanned helicopter\u201d, World Automation Congress (WAC\u2010ISORA), Seville, Spain, Vol. 15, pp. 461\u20106."},{"key":"key2022021620472233500_b4","doi-asserted-by":"crossref","unstructured":"del Cerro, J., Valero, J. and Barrientos, A. 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(2004), \u201cMechanical model and control of an auton mous small sized helicopter with a stiff main rotor\u201d, Proceedings of 2004 IEEE\/RSJ International Conference on Intelligent Robots Systems, Sandal, Japan, IEEE, pp. 2469\u201074."},{"key":"key2022021620472233500_b6","unstructured":"Ljung, L. (1999), System Identification: Theory for the User, 2nd ed., Prentice\u2010Hall, Upper Saddle River, NJ, pp. 199\u2010203."},{"key":"key2022021620472233500_b14","doi-asserted-by":"crossref","unstructured":"Malhotra, R., Singh, N. and Singh, Y. (2011), \u201cGenetic algorithms: concepts, design for optimization of process controllers\u201d, Computer and Information Science, Vol. 4 No. 2, pp. 39\u201054.","DOI":"10.5539\/cis.v4n2p39"},{"key":"key2022021620472233500_b15","unstructured":"Putschoegl, W. (2010), \u201cOn calibrating stochastic volatility models with time\u2010dependent parameters\u201d, Quantitative Finance Papers from arXiv.org."},{"key":"key2022021620472233500_b2","unstructured":"Yang, F., Xiong, X., Chen, Z. and Zhang, P. (2010), \u201cModeling, system identification and validation of small rotorcraft based unmanned aerial vehicle\u201d, Journal of Beijing University of Aeronautics and Astronautics, Vol. 36 No. 8, pp. 914\u201017."},{"key":"key2022021620472233500_b17","unstructured":"Yaou, Z., Tiansheng, L. and Fujian, D. (2007), \u201cThe pitch and roll attitude modeling and stable control of unmanned helicopter\u201d, Journal of Shanghai Jiaotong University, Vol. 41 No. 1, pp. 100\u20103."},{"key":"key2022021620472233500_frd1","unstructured":"Shim, D.H., Kim, H.J. and Satry, S. 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