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

• 成果类型: Article
• 署名作者: Ortega, Romeo; Aranovskiy, Stanislav; Pyrkin, Anton A.; Astolfi, Alessandro; Bobtsov, Alexey A.
• 署名单位: Instituto Tecnologico Autonomo de Mexico; ITMO University; ITMO University; Hangzhou Dianzi University; Imperial College London; University of Rome Tor Vergata
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3003651
• 发表日期: 2021
• 页码: 2265-2272
• 关键词: convergence; transient analysis; Adaptive control; Mathematical model; Linear systems; Linear Regression; Adaptation models; Adaptive systems; estimation; System identification

摘要

We present some new results on the dynamic regressor 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 the following, first, a unified treatment of the continuous and the discrete-time cases; second, 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, third, 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.