Unequal limit cuckoo optimization algorithm applied for optimal design of nonlinear field calibration problem of a triaxial accelerometer

摘要

A triaxial microelectromechanical system (MEMS) accelerometer is a low-cost sensor for measuring the acceleration. However, the measured values by the sensor are generally noisy and inaccurate. Therefore, calibration algorithms need to be used for the calibration of MEMS accelerometers, such as the field calibra- tion. In the case of an accelerometer, using the magnitude of the gravity vector as a stable reference leads to a nonlinear optimization problem. In this paper, a modified version of the Cuckoo Optimization Algorithm (COA), namely Unequal Limit COA (ULCOA), is introduced to achieve the optimal calibration parameters. Then, its performance is evaluated via a set of nonlinear benchmark functions indicating outperformance of the ULCOA in comparison with the particle swarm optimization and the genetic algorithms in terms of accuracy and robustness. Afterward, the ULCOA-based field calibration for the triaxial MEMS accelerometer is discussed. Finally, experimental results are provided and compared with other calibration methods. (C) 2020 Elsevier Ltd. All rights reserved.