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New Results on Parameter Estimation via Dynamic Regressor Extension and Mixing: Continuous and Discrete-time Cases

Abstract : We present some new results on the dynamic re-gressor extension and mixing parameter estimators for linear regression models recently proposed in the literature. This technique has proven instrumental in the solution of several open problems in system identification and adaptive control. The new results include: (i) a unified treatment of the continuous and the discrete-time cases; (ii) the proposal of two new extended regressor matrices, one which guarantees a quantifiable transient performance improvement, and the other exponential convergence under conditions that are strictly weaker than regressor persistence of excitation; and (iii) an alternative estimator ensuring convergence in finite-time whose adaptation gain, in contrast with the existing one, does not converge to zero. Simulations that illustrate our results are also presented.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02948492
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Submitted on : Friday, September 25, 2020 - 1:31:55 PM
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Romeo Ortega, Stanislav Aranovskiy, Anton Pyrkin, Alessandro Astolfi, Alexey Bobtsov. New Results on Parameter Estimation via Dynamic Regressor Extension and Mixing: Continuous and Discrete-time Cases. IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. ⟨10.1109/TAC.2020.3003651⟩. ⟨hal-02948492⟩

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