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 through
several simulation examples, including two benchmark processes (namely,
a distillation column and a pH-neutralization process).