The paper introduces novel techniques for achieving global convergence results and improving transient performance in stochastic adaptive control. For example, there is introduced switching between a least squares and an extended least squares parameter estimation algorithm according to an ill-conditioning measure, and there is appropriate selection of external persistently exciting signals, th...

In this paper, unfalsified control theory (a data-driven model-free control theory) is applied to determine which control parameter sets in a specified control structure are able to meet a given performance specification, using merely measured input/output data. The need for a finite, often large, amount of parameter sets (“gridding”) is overcome by applying an ellipsoidal description of the re...

One of the most widely used gradient-based adaptation algorithms is the so called normalized least mean square (NLMS) algorithm. The rate of convergence, misadjustment and noise insensitivity of the NLMS-type algorithm depend on the proper choice of the step size parameter, which controls the weighting applied to each coefficient update. Different step size methods have been proposed to improve...

We study numerical approximations for the payoff function of the stochastic optimal stopping and control problem. It is known that the payoff function of the optimal stopping and control problem corresponds to the solution of a normalized Bellman PDE. The principal aim of this thesis is to study the rate at which finite difference approximations, derived from the normalized Bellman PDE, converg...

In this letter note we present two new parameter identifiers whose estimates converge in finite time under weak interval excitation assumptions. The main novelty is that, contrast with other finite-convergence (FCT) estimators, our schemes preserve the FCT property when parameters change. previous versions of estimators can track variations only asymptotically. Continuous-time and discrete-time...

This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the ...

A Lavrentiev prox-regularization method for optimal control problems with pointwise state constraints is introduced where both the objective function and the constraints are regularized. The convergence of the controls generated by the iterative Lavrentiev prox-regularization algorithm is studied. For a sequence of regularization parameters that converges to zero, strong convergence of the gene...