A sufficient condition for the convergence of adaptive observer in continuous-time model identification

Abstract

This paper proposes a new method to identify a continuous-time model from sampled I/O data by using an adaptive observer. The boundedness of the parameter estimate and the exponential convergence of the parameter estimation error to 0 under the assumption of Persistently exciting (PE) signals are guaranteed. In order to identify the plant from a finite number of I/O data, an adaptive observer for a backward system is also proposed. In noise-free cases, the magnitude of the parameter estimation error can be made arbitrarily small if the sampling period is smaller than a certain value.