A Model-Based Approach for Measurement Noise Estimation and Compensation in Feedback Control Systems

Abstract

This article considers the problem of measurement noise rejection in a linear output-feedback control system. Specifically, we take into account not only the rejection of high-frequency stochastic noises but also the compensation for low-frequency measurement errors, such as bias and drift, which cannot be well-handled by the classic frequency-domain filters or Kalman filters. A novel noise estimator (NE)-based robust control solution is proposed. The NE is designed in the frequency domain by exploiting the system model and control structure information and is embedded into the controller instead of being an independent functional module in the closed-loop system. The adverse effects of model uncertainties on the performance of the NE-based solution are investigated, and an improved solution is proposed by incorporating a simple low-pass filter as the prefilter of NE. This solution is applied to the angle tracking problem of a 2-DOF experimental helicopter platform equipped with a low-cost and low-accuracy microelectromechanical system (MEMS) inertial measurement unit (IMU) (MEMS IMU) for angular position/rate measurements. Both numerical simulation and experimental comparisons with other existing approaches demonstrate: 1) constant bias and time-varying drift in the IMU measurements are systematically addressed by the solution; 2) it is accessible to improve the steady-state tracking accuracy by tuning the parameter of NE to extend its bandwidth; and 3) when model uncertainties limit the feasible bandwidth of NE, the improved solution is able to largely maintain its noise rejection performance.