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In this note a new high performance least squares parameter estimator isproposed. The main features of the estimator are: (i) global exponentialconvergence is guaranteed for all identifiable linear regression equations;(ii) it incorporates a forgetting factor allowing it to preserve alertness totime-varying parameters; (iii) thanks to the addition of a mixing step itrelies on a set of scalar regression equations ensuring a superior transientperformance; (iv) it is applicable to nonlinearly parameterized regressionsverifying a monotonicity condition and to a class of systems with switchedtime-varying parameters; (v) it is shown that it is bounded-input-bounded-statestable with respect to additive disturbances; (vi) continuous and discrete-timeversions of the estimator are given. The superior performance of the proposedestimator is illustrated with a series of examples reported in the literature.

A New Least Squares Parameter Estimator for Nonlinear Regression Equations with Relaxed Excitation Conditions and Forgetting Factor