Online Error Compensation of Accelerometer While Drilling Based on MISO

Aiming at the error compensation of the accelerometer in drilling, an online accelerometer error compensation method based on a magnetic inertial shark optimizer (MISO) is proposed. First, the error compensation model is established by analyzing the error of accelerometer. Then, the objective function is constructed according to the relationship between the local gravitational acceleration and the theoretical mode value, and the constraint conditions of gravity angle and magnetic-gravity angle are designed by the gyroscope and magnetometer, respectively. In MISO, a magnetometer observation gravity vector model is constructed to predict the optimal solution position; with this position as the center, a fixed-point explosion strategy is proposed to improve the global search ability of error parameters and design a gravity-oriented factor based on the local gravity vector and the fitness value of the current solution to dynamically control the local search. Additionally, an intelligent escape optimal solution search behavior is designed to distinguish whether it is trapped in the local optimal according to the dispersion degree of the historical position of the current solution, and the success rate and speed of escaping the local optimal are improved by position updating simplification and optimal region mutation. Finally, the experimental results show that compared with the Genghis khan shark optimizer (GKSO), the convergence speed of MISO is increased by about 26%, and the average absolute error of inclination is reduced from 3.6° to 0.7°, which improves the measuring accuracy of the accelerometer in drilling.