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High-order tuners are algorithms that show promise in achieving greaterefficiency than classic gradient-based algorithms in identifying the parametersof parametric models and/or in facilitating the progress of a control oroptimization algorithm whose adaptive behavior relies on such models. Forhigh-order tuners, robust stability properties, namely uniform globalasymptotic (and exponential) stability, currently rely on a persistentexcitation (PE) condition. In this work, we establish such stability propertieswith a novel analysis based on a Matrosov theorem and then show that the PErequirement can be relaxed via a concurrent learning technique driven bysampled data points that are sufficiently rich. We show numerically thatconcurrent learning may greatly improve efficiency. We incorporate resetmethods that preserve the stability guarantees while providing additionalimprovements that may be relevant in applications that demand highly accurateparameter estimates at relatively low additional cost in computation.

Concurrent learning in high-order tuners for parameter identification