Skew Filtering for Online State Estimation and Control

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 2; pp. 1508 - 1515
• Main Authors: Dogru, Oguzhan, Chiplunkar, Ranjith, Huang, Biao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-02-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Closed-skew normal (CSN) distribution; Filtering; Gaussian distribution; Informatics; Kalman filtering (KF); Kalman filters; Metric space; model predictive control; Noise measurement; Normal distribution; Optimization; Probability density function; reinforcement learning (RL); Skewed distributions; State estimation; Wasserstein distance (WD)
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2023.3278881

Process control can become challenging when the measurements are affected by irregular noise. Classical approaches utilize Gaussian methods to alleviate the sensory noise. However, many industries involve skewed noise in their processes. While the closed skew-normal (CSN) distribution generalizes a Gaussian distribution with additional parameters, its dimension increases during recursive estimation, making it impractical. Even though there are some techniques for the solution, they are typically too complicated or inaccurate for higher-dimensional problems. This study proposes a novel online optimization scheme to reduce the dimensionality of a CSN distribution while considering the properties of the complete empirical distribution. Since the objective function used during the optimization step considers the geometry of the metric space, the proposed scheme achieves higher accuracy without sacrificing computational efficiency. The proposed filter is applied to two pilot-scale experiments. The results indicate that it is beneficial for recursive state estimation in the presence of skewed noise.