On parameter tuning and convergence properties of the DREM procedure

The recently proposed Dynamic Regressor Extension and Mixing (DREM) procedure has been proven to enhance transient performance in online parameter estimation and it has been successfully applied to a variety of adaptive control problems and applications. However, to use this procedure, a linear operator has to be chosen to perform the dynamic extension. A poor choice of the operator can reduce excitation of signals and hence it can compromise convergence properties. This paper presents a systematic selection of operators such that the excitation is always preserved. The paper also studies convergence conditions when the DREM procedure is combined with a least-squares estimator.

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