Control-relevant identification of Volterra series models

This paper considers the control-relevant identification of nonlinear models for nonlinear systems. The discussion covers: Volterra series models, uncertainty modeling for the truncation error and kernel mismatch, robustness analysis of a nonlinear internal model control system, and development of a control-relevant identification criterion. The criterion is expressed by the uncertainty coefficient and the local gain of an inverse Volterra series in such a way that the identified nominal model enables the design of a high performance controller for the plant. A numerical example is presented to illustrate the proposed technique.