From FRIT of a PD feedback loop to process modelling and control system design

Shigemasa, Takashi; Negishi, Yasunori; Baba, Yuya

doi:10.3182/20130703-3-fr-4038.00035

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…On the other hand, data-driven tuning methods for linear systems have gained much attention because they can tune control parameters in a simple manner. Typical examples of these methods are virtual reference feedback tuning (VRFT) − and fictitious reference iterative tuning (FRIT). − A data-driven self-tuning controller is also proposed . One of the advantages of data-driven tuning methods is that only one-shot closed-loop data can be used to calculate control parameters.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of a Database-Driven PID Controller with Data-Driven Updating Algorithm

Wakitani

Yamamoto

Gopaluni

2019

Ind. Eng. Chem. Res.

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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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Section: Introductionmentioning

confidence: 99%

Design and Application of a Database-Driven PID Controller with Data-Driven Updating Algorithm

Wakitani

Yamamoto

Gopaluni

2019

Ind. Eng. Chem. Res.

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“…Moreover, modeling control objectives is nontrivial and modeling errors are difficult to avoid. As a solution, the fictitious reference iterative tuning (FRIT) algorithm is utilized in the proposed method [7,8]. FRIT is a method used to calculate control parameters directly by using closed-loop data so that the influence of modeling errors can be minimized and the control parameters can be obtained in an off-line manner.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a hierarchical clustering‐based CMAC‐PID controller using closed‐loop data

Liao

Koiwai

Yamamoto

2018

IEEJ Transactions Elec Engng

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A cerebellar model articulation controller (CMAC) neural network is an excellent choice for optimizing the control performance for nonlinear systems. CMAC can be used to generate control inputs and controller parameters, acting as a controller parameter 'tuner'. The most desirable feature of a CMAC is its generalization ability. When CMAC is utilized as a controller parameter 'tuner', the CMAC-based controller facilitates the improvement of control performance for untrained signals. A conventional CMAC achieves higher accuracy by increasing the number of labels for each weight table. However, as the number of labels increases, the requirement for memory increases rapidly and generalization ability decreases. Thus, a CMAC in which the number of labels for each weight table can be decided individually is proposed, allowing layers with more labels to boost accuracy and layers with less labels to improve the generalization ability of the CMAC. Furthermore, hierarchical clustering is employed to determine the labels in each weight table, allowing the input space to be quantized adaptively. Additionally, the output of a CMAC is controller gain, and fictitious reference iterative tuning (FRIT) is employed to adjust the weights of CMAC in an offline manner. In this work, the controller used is a proportional-integral-derivative (PID) controller. Finally, the effectiveness of the proposed method is verified numerically through some simulations and experiments.

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