A new Minimum Variance Observer for Stochastic LPV systems with Unknown Inputs*. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Meyer, L.; Ichalal, D.; Vigneron, V.; Vasiljevic, C.; Oswald, J.
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 4947
• Page End: 4953
• Publication Date: 2017-07
• Subjects: LPV Systems -- Unknown Input Observer -- Kalman Filter -- Dynamical Systems -- Discrete-time Systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.756
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper is dedicated to the design of a state estimator for discrete-time Linear Parameter Varying (LPV) systems affected by unknown inputs and random Gaussian noises. Contrary to the existing work, the observer designed in this paper takes measures at several time steps into account in order to improve the performance (in terms of minimizing the variance estimation error). This approach is based on combining the classical Kalman Filter with the design strategies of deterministic observer for LPV systems in deterministic framework. Then, as an extension of this result, the observer is used for estimation of LPV systems without unknown inputs when state noises have a very high variance in comparison to the measurement noises. Simulation results are presented to illustrate the effectiveness of the proposed approach.