Adaptive Filter Based on Model Residual Weight Self-Updating for Electromagnetic-Driven Micromirror

The electromagnetic-driven micromirror (EDMM) is contaminated with noise measured by piezoresistive sensors located in the basement of the chip during the process of angle positioning. In this article, a novel residual weight adaptive filter (RWAF) for EDMM is proposed to deal with the problem of noise pollution and eliminating outliers. Above all, the so-called Hammerstein architecture embedded with a rate-dependent Duhem submodel is developed to describe the dynamic performance for EDMM. Then, the residual prediction generated by the random variable is taken as the sample to be estimated, and the confidence interval estimation of the given confidence degree is determined by combining the residual variance. Subsequently, the upper and lower limits of confidence intervals are used as threshold to reduce model error via application of generalized gradient that is added in the Kalman gain for compensating the nonlinear part to improve filtering accuracy. In addition, convergence analysis of the novel algorithm is analyzed using martingale convergence theorem. Finally, conducted quantitative comparisons with unscented Kalman filter (UKF) and extended KF (EKF) reveal the advantages of the proposed RWAF in noise attenuation and state reconstruction.