Concurrent learning for parameter estimation using dynamic state-derivative estimators
Abstract
A concurrent learning (CL)-based parameter estimator is developed to identify the unknown parameters in a linearly parameterized uncertain control-affine 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 concurrent learning. Since purging results in a discontinuous parameter adaptation law, the closed-loop error system is modeled as a switched system. Asymptotic convergence of the error states to the origin is established under a persistent excitation condition, and the error states are shown to be ultimately bounded under a finite excitation condition. Simulation results are provided to demonstrate the effectiveness of the developed parameter estimator.
Cite
@article{arxiv.1507.08903,
title = {Concurrent learning for parameter estimation using dynamic state-derivative estimators},
author = {Rushikesh Kamalapurkar and Ben Reish and Girish Chowdhary and Warren E. Dixon},
journal= {arXiv preprint arXiv:1507.08903},
year = {2017}
}