1,694
Views
1
CrossRef citations to date
0
Altmetric
CHEMICAL ENGINEERING

A small fixed-wing UAV system identification using metaheuristics

, , , , , , & show all
Article: 2114196 | Received 30 May 2022, Accepted 13 Aug 2022, Published online: 30 Aug 2022

References

  • Abdullah-Al-Shafi, M., Bahar, A. N., & Ai, Q. (2016). Optimized design and performance analysis of novel comparator and full adder in nanoscale. Cogent Engineering, 3(1), 1237864. https://doi.org/10.1080/23311916.2016.1237864
  • Bagherzadeh, S. A. (2018). Nonlinear aircraft system identification using artificial neural networks enhanced by empirical mode decomposition. Aerospace Science and Technology, 75, 155–17. https://doi.org/10.1016/j.ast.2018.01.004
  • Buch, H., Trivedi, I. N., Jangir, P., & Zheng, P. (2017). Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation. Cogent Engineering, 4(1), 1286731. https://doi.org/10.1080/23311916.2017.1286731
  • Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18. https://doi.org/10.1016/j.swevo.2011.02.002
  • Digehsara, P. A., Chegini, S. N., Bagheri, A., Roknsaraei, M. P., & Laili, Y. (2020). An improved particle swarm optimization based on the reinforcement of the population initialization phase by scrambled Halton sequence. Cogent Engineering, 7(1), 1737383. https://doi.org/10.1080/23311916.2020.1737383
  • Durrant-Whyte, H. (2001). Introduction to estimation and the Kalman filter. Australian Centre for Field Robotics, 28(3), 65–94. https://www.dynsyslab.org/archive/RecEst2010/www.idsc.ethz.ch/Courses/Archives/Recursive_Estimation/recursive_filtering_2010/EstimationNotes.pdf.
  • Edoziuno, F. O., Adediran, A. A., Odoni, B. U., Akinwekomi, A. D., Adesina, O. S., Oki, M., & Brando, G. (2020). Optimization and development of predictive models for the corrosion inhibition of mild steel in sulphuric acid by methyl-5-benzoyl-2-benzimidazole carbamate (mebendazole). Cogent Engineering, 7(1), 1714100. https://doi.org/10.1080/23311916.2020.1714100
  • Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110-111, 151–166. http://dx.doi.org/10.1016/j.compstruc.2012.07.010
  • Fekih, A., Xu, H., & Chowdhury, F. N. (2007). Neural networks based system identification techniques for model based fault detection of nonlinear systems. International Journal of Innovative Computing, Information and Control, 3(5), 1073–1085 https://www.academia.edu/download/44104686/Neural_Networks_Based_System_Identificat20160325-31241-ai811p.pdf.
  • Ghosh Roy, A., & Peyada, N. K. (2017). Aircraft parameter estimation using hybrid neuro fuzzy and artificial bee colony optimization (HNFABC) algorithm. Aerospace Science and Technology, 71, 772–782. https://doi.org/10.1016/j.ast.2017.10.030
  • Gim, H., Lee, B. J., Huh, J., Kim, S., & Suk, J. (2018). Longitudinal system identification of an avian-type UAV considering characteristics of actuator. International Journal of Aeronautical and Space Sciences, 19(4), 1017–1026. https://doi.org/10.1007/s42405-018-0084-5
  • Grauer, J. A., & Morelli, E. A. (2015). A new formulation of the filter-error method for aerodynamic parameter estimation in turbulence. 1–19.
  • Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11(1), 1–18. https://doi.org/10.1162/106365603321828970
  • Hoshiya, M., & Saito, E. (1984). Structural identification by extended kalman filter By Masaru Hoshiya 1 and Etsuro Saito 2. Journal of Engineering Mechanics, 110(12), 1757–1770. https://doi.org/10.1061/(ASCE)0733-9399(1984)110:12(1757)
  • Kabaila, P. (1983). On output-error methods for system identification. IEEE Transactions on Automatic Control, 28(1), 12–23. https://doi.org/10.1109/TAC.1983.1103141
  • Kirkpatrick, K., May, J., & Valasek, J. (2013). Aircraft system identification using Artificial Neural Networks. 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition (pp. 679–688).
  • Korkmaz, H., Ertin, O. B., Kasnakoǧlu, C., & Kaynak, Ü. (2013). Design of a flight stabilizer system for a small fixed wing unmanned aerial vehicle using system identification. IFAC Proceedings Volumes, 46(25), 145–149. https://doi.org/10.3182/20130916-2-TR-4042.00012
  • Li, X., & Yin, M. (2014). Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm. Nonlinear Dynamics, 77(1–2), 61–71. https://doi.org/10.1007/s11071-014-1273-9
  • Mirjalili, S. (2015a). The ant lion optimizer. Advances in Engineering Software, 83, 80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010.
  • Mirjalili, S. (2015b). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. http://dx.doi.org/10.1016/j.knosys.2015.07.006
  • Mirjalili, S. (2016a). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073. https://doi.org/10.1007/s00521-015-1920-1
  • Mirjalili, S. (2016b). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.022
  • Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. http://dx.doi.org/10.1016/j.advengsoft.2013.12.007
  • Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), 805–820. https://doi.org/10.1007/s10489-017-1019-8
  • Morelli, E. A. (2006). Practical aspects of the equation-error method for aircraft parameter estimation. AIAA Atmospheric Flight Mechanics Conference and Exhibit 21 August 2006 - 24 August 2006 Keystone, Colorado, 6144. https://doi.org/10.2514/6.2006-6144.
  • Naser, M. Z., & Kodur, V. K. (2020). Concepts and applications for integrating unmanned aerial vehicles (Uav’s) in disaster management. Advances in Computational Design, 5(1), 91–109. http://dx.doi.org/10.12989/acd.2020.5.1.091.
  • Sanders, F. C., Tischler, M. B., Berger, T., Berrios, M. G., & Gong, A. (2018). System identification and multi-objective longitudinal control law design for a small fixed-wing UAV. 2018 AIAA Atmospheric Flight Mechanics Conference 8–12 January 2018 Kissimmee, Florida. https://doi.org/10.2514/6.2018-0296
  • Shuaibu Hassan, A., Sun, Y., & Wang, Z. (2020). Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends. Cogent Engineering, 7(1), 1766394. https://doi.org/10.1080/23311916.2020.1766394
  • Srivastava, A., Kumar, A., & Kanti Ghosh, A. (2019). Estimation of longitudinal aerodynamic derivatives using genetic algorithm optimized method. American Journal of Engineering and Technology Management, 4(2), 34. https://doi.org/10.11648/j.ajetm.20190402.11
  • Tanabe, R., & Fukunaga, A. S. (2014). Improving the search performance of SHADE using linear population size reduction. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1658–1665).
  • Valasek, J. (2013). Small unmanned aircraft: Theory and practice Randal W. Beard and Timothy W. McLain, Princeton Univ. Press, Princeton, NJ, 2012, 320 pp., $99.50. Journal of Guidance, Control, and Dynamics, 36(1), 344–345. https://doi.org/10.2514/1.61067
  • Viktorin, A., Pluhacek, M., & Senkerik, R. (2016). Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set. 2016 IEEE Congress on Evolutionary Computation (CEC) 24-29 July 2016. Vancouver, BC, Canada. , 4797–4803. https://doi.org/10.1109/CEC.2016.7744404 .
  • Wan, E. A., & Van Der Merwe, R. (2000). The unscented Kalman filter for nonlinear estimation. Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat No00EX373) (pp. 153–158).
  • Wei, P., Gao, C., & Jing, W. (2019). Longitudinal aerodynamic coefficients estimation and identifiability analysis for hypersonic glider controlled by moving mass. International Journal of Aeronautical and Space Sciences, 20(1), 31–43. https://doi.org/10.1007/s42405-018-0123-2
  • Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4, 65–85. https://doi.org/10.1007/BF00175354
  • Zhang, J., & Sanderson, A. C. (2009). JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5), 945–958. https://doi.org/10.1109/TEVC.2009.2014613