This study deals with an updating algorithm for a database-driven proportional-integral-derivative (DD-PID) controller that uses a database for tuning control parameters. PID controllers are still used in many process systems including chemical processes. However, if systems exhibit nonlinearity, PID controllers with fixed PID parameters cannot achieve the desired control performance when the systems' equilibrium points are changed by set point changes. The DD-PID controller has been proposed to solve this problem. This controller can realize good control performance for nonlinear systems because it updates the PID parameters in its database so that the control performance around each equilibrium point has a desired characteristic. However, many experiments have to be performed for this controller to obtain a suitable database. This paper proposes a new offline database updating method called data-driven extended fictitious reference iterative tuning (DD-E-FRIT) method. In this method, the E-FRIT method, which is a direct control parameter tuning method for the linear system, is applied to the updating rule of the DD-PID controller. The DD-E-FRIT method can update a database offline and obtain the database for realizing the desired tracking property of a closed-loop system by storing one-shot operating data into the database.

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Modeling and prediction of molecular weight by MBR.

This paper describes the design of an adaptive output feedback control system in discrete-time, based on almost strictly positive real (ASPR)-ness with a feedforward input. It is well-known that an adaptive output feedback control system based on ASPR conditions can achieve asymptotic stability via a constant feedback gain. Unfortunately, most realistic systems are not ASPR because of the severe conditions. The introduction of a parallel feedforward compensator (PFC) is an efficient way to alleviate such restrictions. However, the problem remains that there exists a steady state error between the output of the augmented system and the output of the original system. The proposed scheme provides a strategy wherein the feedforward input is utilized such that the steady state error is removed. Furthermore, the fictitious reference iterative tuning (FRIT) approach is employed to determine the control parameters using one-shot input/output experimental data directly, without prior information about the control system. This paper explains how the FRIT approach is applied in designing an adaptive output feedback control system. The effectiveness of the proposed scheme is confirmed experimentally, by using a motor application.