The content presents a novel online continuous-time parameter estimator for linear systems affected by unknown but bounded perturbations. The key highlights are:
The estimator is designed by augmenting the Dynamic Regressor Extension and Mixing (DREM) procedure with an Instrumental Variables (IV) based extension scheme and filtering with averaging.
This approach ensures that the perturbation in the new regression equations asymptotically vanishes, even if the original disturbance and system regressor are dependent.
The gradient-based estimator designed on the scalar regression equations obtained through DREM guarantees online unbiased asymptotic identification of the system parameters under weak independence and excitation assumptions.
Compared to existing approaches, the proposed estimator ensures exact asymptotic parameter convergence (P1) with exponential rate in the perturbation-free case (P2), and has relaxed convergence conditions (P3).
The theoretical results are supported by adequate numerical simulations, which demonstrate the superior performance of the proposed estimator over standard approaches.
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