MHE Based State and Parameter Estimation for Systems subjected to Non-Gaussian Disturbances. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Varshney, Devyani; Patwardhan, Sachin C.; Bhushan, Mani; Biegler, Lorenz T.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 5940
• Page End: 5945
• Publication Date: 2020
• Subjects: Moving horizon estimation -- Bayesian estimation -- State -- parameter estimation -- Non-Gaussian disturbances; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1646
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Moving horizon estimation (MHE) is a popular state estimation technique, particularly due to its similarity with model predictive control. The probabilistic formulation of the conventional MHE is developed under the simplifying assumption that state disturbances and measurement noise densities are Gaussian. However, many systems of interest are subjected to uncertainties that have non-Gaussian densities. In current work, we formally extend an existing probabilistic Bayesian formulation of MHE [Varshney et al., 2019] to simultaneous state and parameter estimation for systems subjected to non-Gaussian uncertainties in the state dynamics and measurement model. The efficacy of the proposed MHE has been demonstrated by conducting stochastic simulation studies on a system subjected to non-Gaussian densities. Analysis of simulation results reveals that the estimation performance of the proposed MHE formulation is superior to estimation performances of the conventional Bayesian estimators that can handle non-Gaussian densities and employ the random walk model for parameter variations.