Parameter identification and state estimation for continuous-time nonlinear systems

Abstract

This paper deals with a new method concerning parameter identification designed for nonlinear uncertain systems. The parameter identification algorithm is obtained by inverting the mapping between the vector of unknown parameters and the vector of states. Then, this parameter law depends strongly on the values of the output and its successive derivatives which could be restored through a Variable Structure Observer (VSO) converging in a finite time. Thanks to this latter property, it is possible to guarantee a parameter identification law which converges also in a finite time to the nominal values of the parameters without the use of the classical persistent excitation usually required for the input. The main interest of our approach is its robustness with respect to parameter uncertainties.