Composite adaptation and learning techniques were initially proposed for improving
parameter convergence in adaptive control and have generated considerable research …

Conclusion In this study, for a class of uncertain high-order SFNSs, an adaptive DSC
method is proposed. To avoid the “differential explosion” problem, multiple (if necessary) first …

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 …

In this article, we consider the excitation preservation problem of Kreisselmeier's regressor
extension scheme. We analyze this problem within the context of the dynamic regressor …

In this paper we propose a new parameter estimator that ensures global exponential
convergence of linear regression models requiring only the necessary assumption of …

In this article, an adaptive dynamic surface control approach is proposed for uncertain strict‐
feedback systems (SFSs) to guarantee both the prescribed transient tracking performance …

Least-squares parameter estimation is important in system identification and adaptive
control owing to its enhanced performance and robustness compared to gradient-descent …

Least squares estimation is appealing in performance and robustness improvements of
adaptive control. A strict condition termed persistent excitation (PE) needs to be satisfied to …

To circumvent the potentially poor transient response induced by nonlinear uncertain
dynamics in the adaptive control system, this article proposes a new model reference …

Online data memory is essential for adaptive estimation and control as it can enhance the
performance and robustness of adaptive systems compared to adaptive systems without …