Estimation of parameter space representation for mechanical systems with nonlinear friction

Abstract

The projection type iterative learning identification method has several advantages such as: (i) no time-derivatives of input/output signals are required and (ii) it gives unbiased estimations. However, this identification method requires a parameterized model obtained by projecting a tracking error signal onto a finite-dimensional subspace, and the parameterized model must be estimated in advance. The model is called a parameter space representation in this paper. This paper presents an approach for estimating the parameter space representation of a system with nonlinear friction. Its effectiveness is demonstrated through numerical example of a linear plant with nonlinear friction.