Concurrent Learning for Parameter Estimation Using Dynamic State-Derivative Estimators

Rushikesh Kamalapurkar, Benjamin Reish, Girish Chowdhary, Warren E. Dixon

Research output: Contribution to journalArticlepeer-review


A concurrent learning (CL)-based parameter estimator is developed to identify the unknown parameters in a nonlinear system. Unlike state-of-the-art CL techniques that assume knowledge of the state derivative or rely on numerical smoothing, CL is implemented using a dynamic state-derivative estimator. A novel purging algorithm is introduced to discard possibly erroneous data recorded during the transient phase for CL. Asymptotic convergence of the error states to the origin is established under a persistent excitation condition, and the error states are shown to be uniformly ultimately bounded under a finite excitation condition.

Original languageEnglish (US)
Article number7858671
Pages (from-to)3594-3601
Number of pages8
JournalIEEE Transactions on Automatic Control
Issue number7
StatePublished - Jul 2017


  • Adaptive systems
  • Lyapunov methods
  • concurrent learning
  • observers
  • parameter estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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