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Data-Driven Nonlinear Control Design Using Virtual-Reference Feedback Tuning Based on the Block-Oriented Modeling of Nonlinear Systems
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DOI：10.1021/acs.iecr.8b00809.png)
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Abstract
En 中文
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-lineanty (or its inverse), represented by the B-spline series, requires estimation. Moreover, in the method, the non-lineanty 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).
Keywords:
ITERATIVE LEARNING CONTROL
PREDICTIVE CONTROL
NEURAL-NETWORKS
PID CONTROLLER
VRFT APPROACH
IDENTIFICATION
WIENER
DISTILLATION
FRAMEWORK
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