| Citation: | DAI Hongde, ZHENG Weiwei, ZHENG Baidong, et al. Calibration of MEMS accelerometer without turntable based on IFOA[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 1959-1968. doi: 10.13700/j.bh.1001-5965.2020.0349(in Chinese)
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In order to improve the calibration efficiency of Micro-Electro-Mechanical System (MEMS) accelerometers and reduce the dependence on high-precision turntables, a MEMS accelerometer calibration method based on Improved Fruit Fly Optimization Algorithm (IFOA) without turntables is proposed. The method first converts the accelerometer calibration problem into a nonlinear function optimization problem according to the principle of norm-observation. Afterwards, in view of the shortcomings of the classic FOA that can only search for positive parameters and search step size is fixed, the smell concentration judgment value and search step size were improved to make IFOA have global parameter search and variable step size. The two improved performances were tested using the Rosenbrock function. The results show that the IFOA has a global parameter optimization range and higher optimization accuracy than the classic FOA. Finally, the IFOA was applied to solve the nonlinear function optimization problem of accelerometer calibration parameters. The results are compared with those of Newton iteration method and Particle Swarm Optimization (PSO) algorithm. The simulation results show that the IFOA is 1-3 orders of magnitude higher than Newton iteration method in terms of solution accuracy. Compared with Newton iteration method and PSO algorithm, the IFOA improves the running stability by 30% and 34% respectively, and reduces the running time by 15.2% and 43.6% respectively. The IFOA has a good application value in the calibration of accelerometer without turntable
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