In this paper a linearly parameterized approximators based algorithm for identifying uncertain systems is proposed. The proposed algorithm ensures stability without previous knowledge of bounds for the ''optimal'' parameter, approximation error, and disturbances. This algorithm guarantees that the state error converges asymptotically to zero, even in the presence of uncertainties, as long as some conditions on the design parameters are satisfied.
Identification; uncertain systems; linearly parameterized approximators
