2021/06/16 by Marina Korotina, Korotina, Marina, José Guadalupe Romero +7
Computer Science · Engineering · Physics and Astronomy · #Adaptive Dynamic Programming Control #Control Systems and Identification #FOS: Electrical engineering #Fault Detection and Control Systems #Model Reduction and Neural Networks #Sparse and Compressive Sensing Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.08773
openalex publication_date 2021/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we prove that it is possible to estimate online the parameters of a classical vector linear regression equation Y=Ωθ, where Y ∈ ℝn, Ω∈ ℝn × q are bounded, measurable signals and θ∈ ℝq is a constant vector of unknown parameters, even when the regressor Ω is not persistently exciting. Moreover, the convergence of the new parameter estimator is global and exponential and is given for both continuous-time and discrete-time implementations. As an illustration example, we consider the problem of parameter estimation of a linear time-invariant system, when the input signal is not sufficiently exciting, which is known to be a necessary and sufficient condition for the solution of the problem with the standard gradient or least-squares adaptation algorithms.