Design of a Data-Driven Controller for a Spiral Heat Exchanger

Abstract

A data-driven proportional-integral-derivative (DD-PID) controller has been proposed as an effective controller for nonlinear systems. The DD-PID controller can tune the PID parameters adaptively at each equilibrium point. In order to train the PID parameters in a database, an offline learning algorithm based on a fictitious reference iterative tuning (FRIT) method was established. This method can compute the PID parameters by using a set of operating data. However, the FRIT method is a control parameter tuning method that is only based on the minimization of the system output in its criterion; therefore, the criterion is insufficient for systems in which the stability of a closed-loop system is important such as chemical process systems because sometimes the sensitivity of an obtained controller becomes high. In order to solve this problem, an extended FRIT (E-FRIT) method that penalizes the input variation in its criterion has been proposed. In this method, the PID parameters that are taken into stability can be calculated. The effectiveness of the proposed method is evaluated by an experimental result of a spiral heat exchanger.