Nonlinear generalized predictive control with virtual unmodeled dynamics decomposition compensation and data driven

Abstract

In this study, a novel nonlinear generalized predictive control (NGPC) method is proposed to tackle the tracking control problem which considers a mathematical model in conjunction with the virtual unmodeled dynamics decomposition compensation technology. First, data-modeling technology is adopted to take advantage of the input and output data information from the process to decompose the virtual unmodeled dynamics into the form of posterior measurement unmodeled dynamics and an unknown increment. Then, feedforward compensator is designed. The essential difference between the proposed data modeling algorithm and existing methods is that the effect of the measurement unmodeled dynamics is eliminated by the feedforward compensator. For the increment of the virtual unmodeled dynamics, a novel estimation algorithm is established through a data driven approach; A nonlinear compensator for the unknown increment of the virtual unmodeled dynamics is designed to suppress the effect of the increment of the virtual unmodeled dynamics on the closed-loop system. Second, the developed compensator is combined with the GPC algorithm to construct a nonlinear generalized predictive controller. Theoretical analysis proves that the proposed modeling and control algorithm have bounded-input bounded-output (BIBO) stability. Finally, an experimental study is conducted, which indicates the effectiveness of the proposed algorithm.