RT - Journal Article T1 - Design of Unknown Input Proportional-Integral Kalman Filter JF - joc-isice YR - 2014 JO - joc-isice VO - 8 IS - 2 UR - http://joc.kntu.ac.ir/article-1-161-en.html SP - 1 EP - 10 K1 - Proportional Integral Kalman Filter K1 - Unknown Input Observer K1 - Stochastic and Deterministic Disturbance K1 - Unbiased Estimation K1 - Consistent Estimation. AB - 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. LA eng UL http://joc.kntu.ac.ir/article-1-161-en.html M3 ER -