In this paper we propose a new state observer design technique for nonlinear systems. It
consists of an extension of the recently introduced parameter estimation-based observer …

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 paper we are interested in the problem of adaptive state observation of linear time-
varying (LTV) systems where the system and the input matrices depend on unknown time …

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 paper we propose an adaptive state observer for a class of nonlinear systems with
unknown parameters. Assuming that the system variables satisfy an algebraic constraint, a …

The existing results mainly focus on the full-information control for converters with constant
power load (CPL). More precisely, it is assumed that all of the states of the systems should …

The problem of estimating constant parameters from a standard vector linear regression
equation in the absence of sufficient excitation in the regressor is addressed. The first step to …

In this article, we propose a new approach to design globally convergent reduced-order
observers for nonlinear control systems via contraction analysis and convex optimization …

In this paper we provide two significant extensions to the recently developed parameter
estimation-based observer design technique for state-affine systems. First, we consider the …

In this paper we propose a new algorithm that estimates on-line the parameters of a
classical vector linear regression equation Y= Ω θ, where Y∈ R n, Ω∈ R n× q are bounded …