Limited‐information learning observer for simultaneous estimation of states and parameters. (30th August 2021)

• Record Type: Journal Article
• Authors: Ran, Dechao; Zhang, Chengxi; Xiao, Bing
• Other Names: Karimi Hamid Reza guestEditor.; Wang Ning guestEditor.; Man Zhihong guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 32:Number 5(2022)
• Page Start: 2780
• Page End: 2790
• Publication Date: 2021-08-30
• Subjects: parameter estimation -- sampled‐data learning observer -- state estimation; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5758
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: The problem of simultaneous estimation of states and uncertainties by using limited information is investigated in this article. A sampled‐data learning observer (SLO) is presented for linear time‐invariant continuous systems, which can achieve successful estimation while only needs intermittent sampled‐data, saves computing resources, and does not require persistent excitation signals. The observer's demand for continuous measurement is reduced, that is, limited‐information is sufficient. Notably, the uncertainty estimation is performed by a learning equation with only simple addition operations, which is particularly suitable for actual digital system scenarios. Simulation results illustrate the effectiveness of the proposed SLO.