Comparative Analysis of a class of Robust Real-Time Identification Methods

Abstract In this paper the problem of robust real-time identification of 1inear dynamic systems with time varying parameters is considered. Supposing two classes of system disturbance distributions several robust identification algorithms are constructed either on the basis of the formulation of minimum variance estimates or the optimization of dynamic stochastic approximation schemes. The comparative analysis of the algorithms is done by Monte Carlo simulations, covering both constant and time-varying parameter cases. The analysis shows successful applications of the robust algorithms and indicates the most suitable methods for practical applications. Some connections between the adaptive and the robust estimations are illustrated.