Abstract
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 language | English (US) |
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Article number | 7858671 |
Pages (from-to) | 3594-3601 |
Number of pages | 8 |
Journal | IEEE Transactions on Automatic Control |
Volume | 62 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2017 |
Keywords
- Adaptive systems
- Lyapunov methods
- concurrent learning
- observers
- parameter estimation
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering