Parametric uncertainties in adaptive estimation and control have been dealt
with, by and large, in the context of linear parameterizations. Algorithms
based on the gradient descent method either lead to instability or inaccur
ate performance when the unknown parameters occur nonlinearly, Complex dyna
mic models are bound to include nonlinear parameterizations which necessita
te the need for new adaptation algorithms that behave in a stable and accur
ate manner. The authors introduce, in this paper, an error model approach t
o establish these algorithms and their global stability and convergence pro
perties. A number of applications of this error model in adaptive estimatio
n and control are included, in each of which the new algorithm is shown to
result in global boundedness. Simulation results are presented which comple
ment the authors' theoretical derivations.