INVERSE NEURO-FUZZY MODEL BASED CONTROLLER DESIGN FOR A PH NEUTRALIZATION PROCESS

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Journal of Scientific Reports-A
Issue:052
INVERSE NEURO-FUZZY MODEL BASED CONTROLLER DESIGN FOR A PH NEUTRALIZATION PROCESS

INVERSE NEURO-FUZZY MODEL BASED CONTROLLER DESIGN FOR A PH NEUTRALIZATION PROCESS

Authors : Talha Burak AKCA, Cenk ULU, Salih OBUT

Pages : 19-34

Doi:10.59313/jsr-a.1197288

View : 42 | Download : 12

Publication Date : 2023-03-29

Article Type : Research Paper

Abstract :Since pH neutralization processes have extremely nonlinear characteristics, controlling it might be difficult. Therefore, a special controller design is needed to handle the high nonlinearities of the process. In this study, an inverse neuro-fuzzy model-based controller insert ignore into journalissuearticles values(NFMBC); design is presented for control of a pH neutralization process insert ignore into journalissuearticles values(NP);. Input-output insert ignore into journalissuearticles values(IO); data set of the process is collected by applying a proper excitation signal. Then, forward and inverse neuro-fuzzy models of the process are constructed by using this data set after a training process. In terms of design simplicity, a two-input-one-output model structure is chosen for both neuro-fuzzy models. These forward and inverse neuro-fuzzy models are used in a nonlinear internal model control insert ignore into journalissuearticles values(NIMC); structure in order to provide robustness against disturbances and model mismatches. To examine the proposed controller\`s performance, simulation studies are carried out under setpoint variation and disturbance conditions. Additionally, the performance of the inverse NFMBC is compared to that of a fuzzy proportional integral derivative insert ignore into journalissuearticles values(FPID); controller with a 7x7 rule base. The results demonstrate that the designed controller provides more effective control performance for setpoint variations and also exhibits higher robustness against disturbances in the acid flow rate than the FPID controller.
Keywords : Fuzzy Model, Inverse Controller, Adaptive Network Based Fuzzy Inference System, pH Neutralization Process, Internal Model Control

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