Genetic adaptive state estimation with missing input/output data - Fang, H; Wu, J; Shi, Y (Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering, 2010)
Abstract This paper presents the application of a genetic algorithm to state estimation for systems subject to randomly missing input/output data. By modelling the missing data in inputs and outputs with binomial processes, a modified Luenberger observer using the reconstructed data is employed for estimation. The proposed algorithm consists of two procedures: 1) Preprocessing the missing input/output data by reconstruction based on autoregressive (AR) modelling; and 2) implementing the genetic algorithm to perform on-line adaptive state estimation from reconstructed data. The effectiveness of the proposed algorithm is verified by numerical simulation.