Identification of linearly overparametrized nonlinear systems

Identification of Linearly Overparametrized Nonlinear Systems

In many practical modeling and control applications, a partial prior knowledge of the structure and the parametrization of the system is available. A typical situation is where the only unknowns of the system are the values of a few physical parameters which enter linearly and/or nonlinearly in the model. In such a situation, it is clear that an approach to the parameter estimation problem...

Recursive Identification of Overparametrized Systems

B. Widrow e( al.. “Stationary and non-stationary learning characteristics of the LMS adaptive filter, ” Proc. IEEE, vol. 64, pp. 1151-1162.1976. M, Sondhi and D. Mitra, “New results on the performance of a well-known class of adaptive filter s,” Proc. IEEE, vol. 64, no, 11, Nov. 1976, A. Weiss and D. Mitra, “Digital adaptive fdtera: Conditions for convergence. effects of noise and errors arisin...

Identification of Nonlinear Systems

Nonlinear Adaptive Tracking-Control Synthesis for General Linearly Parametrized Systems

where y, x, u denote output, state and control variables respectively, α and β are smooth vector fields on n R and R R h n → : a smooth function. It is assumed here also that the functions α and β are unknown or may be estimated with a considerable inaccuracy. Considering the system (1) it is possible (under certain conditions (Fabri & Kadrikamanathan, 2001; Sastry & Isidori, 1989)) to obtain a...

Identification of Nonlinear Systems by Volterra Systems

| In this paper the identiication of non-linear systems by Volterra Systems (VS) and a special class of VS called Linearly decomposable Volterra Systems (LDVS) is considered. The results obtained for diierent nonlinear systems with VS and LDVS are studied in detail. A clear improvement in performance is observed if a LDVS is used instead of a VS.