This paper concerns adaptive estimation of dynamic systems which are nonlin
early parameterized. A majority of adaptive algorithms employ a gradient ap
proach to determine the direction of adjustment, which ensures stable estim
ation when parameters occur linearly. These algorithms, however, do not suf
fice for estimation in systems with nonlinear parameterization. We introduc
e in this paper a new algorithm for such systems and show that it leads to
globally stable estimation by employing a different regression vector and s
electing a suitable step size. Both concave/convex parameterizations as wel
l as general nonlinear parameterizations are considered. Stable estimation
in the presence of both nonlinear parameters and linear parameters which ma
y appear multiplicatively is established. For the case of concave/convex pa
rameterizations, parameter convergence is shown to result under certain con
ditions of persistent excitation. (C) 2000 Elsevier Science Ltd. All rights
reserved.