  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
time-varying 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.

Dynamical Systems