Data-Driven Nonlinear Control Design Using Virtual-Reference Feedback Tuning Based on the Block-Oriented Modeling of Nonlinear Systems

Process nonlinearities impose difficulties for model identification and control-system design. This paper presents a novel data-driven method for nonlinear control design based on the virtual-reference feedback tuning (VRFT) framework and block-oriented modeling of nonlinear systems. Control-design algorithms for Hammerstein, Wiener, and Hammerstein–Wiener systems were systematically developed. The proposed method can be applied to design a nonlinear controller for an unknown plant directly using one-shot input–output data generated by the plant. In the method, identifying a complete dynamic model of the nonlinear system is not necessary; and only the static non-linearity (or its inverse), represented by the B-spline series, requires estimation. Moreover, in the method, the non-linearity estimation and control design processes are performed simultaneously without the need for nonlinear optimization or iterative procedures. The effectiveness of the proposed control design method is demonstrated herein thro...