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A new least squares parameter estimator for nonlinear regression equations with relaxed excitation conditions and forgetting factor
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- معلومة اضافية
- Contributors:
Instituto Tecnológico Autónomo de México (ITAM); ITMO University Russia; The Hong Kong Polytechnic University Hong Kong (POLYU); CentraleSupélec campus de Rennes; Institut d'Électronique et des Technologies du numéRique (IETR); Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - pôle Sciences et technologie; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ); 2019-0898, Ministry of Education and Science of the Russian Federation
- بيانات النشر:
HAL CCSD
Elsevier
- الموضوع:
2022
- Collection:
Université de Nantes: HAL-UNIV-NANTES
- نبذة مختصرة :
International audience ; 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.
- Relation:
hal-03793150; https://hal-centralesupelec.archives-ouvertes.fr/hal-03793150; https://hal-centralesupelec.archives-ouvertes.fr/hal-03793150/document; https://hal-centralesupelec.archives-ouvertes.fr/hal-03793150/file/ortega_scl22_submitted.pdf
- الرقم المعرف:
10.1016/j.sysconle.2022.105377
- Rights:
info:eu-repo/semantics/OpenAccess
- الرقم المعرف:
edsbas.5B4A398C
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