Abstract
We introduce a novel concurrent learning (CL) algorithm designed to solve parameter estimation problems within a user-prescribed time frame and by utilizing alternating datasets during the learning process. The algorithm can tackle applications involving switching data sets (including data sets that are completely uninformative) that are updated in real-time as the algorithm operates. To achieve parameter estimation within a specified time independent of the dataset’s richness, the switching algorithm employs dynamic gains. The main result establishes uniform global exponential ultimate boundedness, with an ultimate bound that shrinks to zero as the magnitude of the measurement disturbances decreases. The stability analysis leverages tools from hybrid dynamical systems theory, along with a recently introduced dilation/contraction argument on the hybrid time domains of the solutions. The algorithm and main results are illustrated via a numerical example.
Published Version
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