Fluctuation suppression for UI-driven bias accuracy enhancement based on micro-machined gyroscopes array

Unknown-input (UI) fluctuations sourcing from external environment severely deteriorate the accuracy of micro-machined gyroscope because of the unpredictable statistical characteristics. To enhance the accuracy of a four-micro-gyros array under UI fluctuations, an array-based consensus strategy (ACS) consisting of a UI-driven bias model, a local estimator, and an array fusion estimator is proposed. The UI-driven bias model is originally constructed as two behavior-contrasted independent items according to different drift characteristics of individual gyro. For the local estimator, a UI-decoupling operation is proposed to transform the variance-unknown UI model implicitly into a linear combination of variance-estimated variables, which transforms the non-stationary model to an equivalent stationary model. For the array fusion estimator, the reconstructed weight coefficient is designed based on the support theory and Markowitz mean-variance theory to evaluate the confidence level of gyros. Experiment results show that the root-mean-square error (RMSE) and Allan bias instability of the estimated angular rate under UIs are 7.9×10⁻³ °/s and 3.87 °/h, which are respectively reduced by 85.1 % and 35.1 % compared with the average original outputs of the gyros array.