Volume 8, Issue 2 (Journal of Control, V.8, N.2 Summer 2014)                   JoC 2014, 8(2): 1-10 | Back to browse issues page

XML Persian Abstract Print

1- Iran university of science and technology
2- Iran University of Science and Technology
Abstract:   (6577 Views)
In this paper, we introduce the proportional-integral kalman filter for discrete time systems with unknown input. The Proportional-Integral observers (PIOs) have good performance in deal with uncertainty in model, while those cannot handle the effect of determinstic unknown inputs. On the other hand, the Unknown Input Kalman filter (UIKF) is sensitive to uncertianty, while it provides unbiased minimum-variance estimation in the presence of unknown input. Here, we introduce Unknown Input Proportional Integral Kalman filter (UIPIKF) as an unbiased minimum-variance estimator in the presence of uncertainty and unknown input in the model. Using a numerical example, the effectivness of the filrer is demonstrated.
Full-Text [PDF 815 kb]   (2359 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2014/10/13 | Accepted: 2015/02/4 | Published: 2015/02/4

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.