Dual‐mode control with neural network based inverse model for a steel pickling process

Abstract

AbstractThis article describes a novel implementation of the dual‐mode (DM) control utilizing a neural network inverse model on a multivariable process (a steel pickling process). This process is highly nonlinear with variable‐interaction, and is multivariable in nature, hence an accurately nonlinear model is required to provide acceptable control. The requirement of a true analytical inverse can be avoided when neural network models are used; they have the ability to approximate both the forward and the inverse system dynamics. Various changes in the open‐loop dynamics are performed before implementation of the inverse neural network modeling technique. DM control based on neural network inverse model strategy is used to design the controllers to control concentration and pH of the process, which is guaranteed to remove steady‐state offset in the controlled variables to obtain the maximum reaction rate and to comply with limits imposed by legislation. The robustness of the proposed DM control is investigated with respect to changes in disturbances and model mismatch. Comparisons are also made with the conventional inverse neural network controller (NNDIC) and other conventional controller proportional‐integral (PI). Simulation results show the superiority of the DM controller in the cases involving disturbance and model mismatch, while the conventional controller gives better results in the nominal case. Copyright © 2007 Curtin University of Technology and John Wiley & Sons, Ltd.