Adaptive state estimation using MRAS techniques--Convergence analysis and evaluation

Three adaptive state observers for discrete-time systems derived from MRAS techniques are presented. While in a deterministic environment all of these schemes converge toward the linear asymptotic observer, when used in a stochastic environment for adaptive state estimation their performances present noticeable differences. The schemes considered in the paper are analyzed both in a deterministic and stochastic environment using the "equivalent feedback representation" (EFR) method and "ordinary differential equation" (ODE) method, respectively. Conditions for the convergence of the estimated parameters to the desired ones in a stochastic environment are given. The connections with adaptive Kalman filters are discussed. A comparative evaluation of these schemes in a deterministic and stochastic environment based on simulations concludes the paper.