2020
Zhu*, Yang; Zhu, Bo; Liu, Hugh H-T; Qin, Kaiyu
A Model-Based Approach for Measurement Noise Estimation and Compensation in Feedback Control Systems Journal Article
In: IEEE Transactions on Instrumentaion & Measurement, pp. 1–29, 2020.
Abstract | Links | BibTeX | Tags: bias and drift compensation, low-cost sensors, measurement noise estimation, mems imu, output feedback systems, robust control
@article{TIM2020YZ,
title = {A Model-Based Approach for Measurement Noise Estimation and Compensation in Feedback Control Systems},
author = {Yang Zhu* and Bo Zhu and Hugh H-T Liu and Kaiyu Qin},
url = {https://ieeexplore.ieee.org/document/9082005/},
doi = {10.1109/TIM.2020.2991290},
year = {2020},
date = {2020-01-01},
journal = {IEEE Transactions on Instrumentaion & Measurement},
pages = {1--29},
abstract = {This paper 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 like bias and drift which cannot be well-handled by 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 NE-based solution are investigated, and an improved solution is proposed by incorporating a simple low-pass filter as the pre-filter 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 MEMS IMU for angular position/rate measurements. Both numerical simulation and experimental comparisons with other existing approaches demonstrate: (i) constant bias and time-varying drift in the IMU measurements are systematically addressed by the solution; (ii) it is accessible to improve the steady-state tracking accuracy by tuning the parameter of NE to extend its bandwidth; and (iii) when model uncertainties limit the feasible bandwidth of NE, the improved solution is able to largely maintain its noise rejection performance.
},
keywords = {bias and drift compensation, low-cost sensors, measurement noise estimation, mems imu, output feedback systems, robust control},
pubstate = {published},
tppubtype = {article}
}
This paper 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 like bias and drift which cannot be well-handled by 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 NE-based solution are investigated, and an improved solution is proposed by incorporating a simple low-pass filter as the pre-filter 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 MEMS IMU for angular position/rate measurements. Both numerical simulation and experimental comparisons with other existing approaches demonstrate: (i) constant bias and time-varying drift in the IMU measurements are systematically addressed by the solution; (ii) it is accessible to improve the steady-state tracking accuracy by tuning the parameter of NE to extend its bandwidth; and (iii) when model uncertainties limit the feasible bandwidth of NE, the improved solution is able to largely maintain its noise rejection performance.