Generalized recursive least squares: Stability, robustness, and excitation

We study a class of recursive least-squares estimators in an errors-in-variables setting where disturbances affect both the regressor and the regressand variables. We prove the existence and stability of an optimal steady state and robustness with respect to the disturbances in form of input-to-state and input–output stability relative to the unperturbed steady-state trajectories. Depending on the choice of some design parameters, different specific estimators can be realized within the considered class, each of which is associated with a different underlying optimization problem and with different excitation requirements for the unperturbed regressor. As expected, we find that persistence of excitation is associated with uniform, in fact exponential, convergence. In addition, we also show that choices of the design parameters are possible for which convergence and robustness hold without persistence of excitation and with the same asymptotic gain, the only difference being a loss of uniformity in the convergence rate.