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A new least squares parameter estimator for nonlinear regression equations with relaxed excitation conditions and forgetting factor

Abstract : In this note a new high performance least squares parameter estimator is proposed. The main features of the estimator are: (i) global exponential convergence is guaranteed for all identifiable linear regression equations; (ii) it incorporates a forgetting factor allowing it to preserve alertness to time-varying parameters; (iii) thanks to the addition of a mixing step it relies on a set of scalar regression equations ensuring a superior transient performance; (iv) it is applicable to nonlinearly parameterized regressions verifying a monotonicity condition and to a class of systems with switched timevarying parameters; (v) it is shown that it is bounded-input-bounded-state stable with respect to additive disturbances; (vi) continuous and discrete-time versions of the estimator are given. The superior performance of the proposed estimator is illustrated with a series of examples reported in the literature.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-03793150
Contributor : Stanislav Aranovskiy Connect in order to contact the contributor
Submitted on : Friday, September 30, 2022 - 4:57:00 PM
Last modification on : Sunday, October 2, 2022 - 3:47:28 AM

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Romeo Ortega, Jose Guadalupe Romero, Stanislav Aranovskiy. A new least squares parameter estimator for nonlinear regression equations with relaxed excitation conditions and forgetting factor. Systems and Control Letters, 2022, 169, pp.105377. ⟨10.1016/j.sysconle.2022.105377⟩. ⟨hal-03793150⟩

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