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HGAPSO: A new aerodynamic parameters identification algorithm for small unmanned aerial vehicles
Received date: 2016-04-25
Revised date: 2016-06-15
Online published: 2016-06-27
Supported by
National High-tech Research and Development Program of China (2014AA2157)
In the development of small unmanned aerial vehicles (UAVs), aerodynamic parameter identification is needed for stability and control analysis and flight control law assessment. An improved hybrid genetic and particle swarm optimization algorithm (HGAPSO) is proposed for aerodynamic parameter identification. In this algorithm, the particle swarm optimization algorithm (PSO) is used as the main body, and the cross-over and mutation operation of genetic algorithm (GA) is included into the optimization of particle path to enhance the ability to jump out of the local optimal path. Kent mapping is also used to improve the initial distribution of the particle population, and to make the distribution more uniform and then the optimization more global. Based on the simulation results, the HGAPSO, PSO and GA algorithms are compared in terms of accuracy of identified aerodynamic parameters and success rate of identification. Monte Carlo simulations are further conducted to evaluate the effect of random noises in the measured signals. The results show that HGAPSO can provide both high efficiency and globalization in optimization, and has good resistance against measured noises. Flight testing data acquired from a small UAV are used to comprehensively evaluate the HGAPSO algorithm,and the HGAPSO shows satisfactory ability to identify aerodynamic parameters based on the flight data.
SHAO Gan , ZHANG Shuguang , TANG Peng . HGAPSO: A new aerodynamic parameters identification algorithm for small unmanned aerial vehicles[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017 , 38(4) : 120365 -120365 . DOI: 10.7527/S1000-6893.2016.0196
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