Design of Nonlinear Adaptive-Predictive Control System with ANFIS Modeling for Urea Plant Reactor Unit

Muhammad, Abduh; Nazaruddin, Yul Y.; Siregar, Parsaulian I.

doi:10.1109/ica.2019.8916735

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…RBNN coupled with MPC was demonstrated to be effective in the paper of Huang et al [16] for clutch control, Han et al [17] for optimization of wind turbines, Mirzaeinejad [18] for controlling of wheel slip in antilock braking systems, Jamil et al [19] for controlling control of vibrations in tall structure. Also, Muhammad et al [20] carried out ANFIS reinforced MPC algorithm for another nonlinear system of urea plant reactor unit and Pang et al [21] for Omni-Directional Service Robot.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

Uluocak¹,

Yavuz²

2023

Computer Systems Science and Engineering

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The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of an Eddy Current Dynamometer based on experimentally obtained system operational data. Finally, the comparison of AI methods and related learning performances based on the assessment technique of mean absolute percentage error (MAPE) issues are discussed. The results indicate that Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), Radial Basis Network (RBNN), Neuro Fuzzy Network (ANFIS) coupled MPC have quite satisfying performances. The presented results indicate that, amongst them, GRNN appears to provide the best performance.

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