Adaptive nonlinear control of pH neutralization processes using fuzzy approximators

Salehi, S.V.; Shahrokhi, Mohammad; Nejati, Ali

doi:10.1016/j.conengprac.2009.06.007

Cited by 34 publications

(12 citation statements)

References 42 publications

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“…5) is constituted essentially of a treatment tank of cross sectional area A, a mixer, acid and base injection pipes, a pH probe, a level sensor to measure the level h in the tank and a discharge valve (Henson and Seborg 1994;Salehi et al 2009). It consists of an acid stream q 1 , buffer stream q 2 and base stream q 3 that are mixed in the tank.…”

Section: Process Descriptionmentioning

confidence: 99%

PWARX Model Identification Based on Clustering Approach

Lassoued

Abderrahim

2014

Complex System Modelling and Control Through Intelligent Soft Computations

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This chapter addresses the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models. In order to overcome the main drawbacks of the existing methods such as their sensitivity to poor initializations and the existence of outliers, we propose the use of the Chiu's clustering algorithm and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. A comparative study of the two proposed approaches with the k-means method is achieved in simulation. The results of experimental validation are also presented to illustrate the effectiveness of the proposed methods.

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Section: Process Descriptionmentioning

confidence: 99%

PWARX Model Identification Based on Clustering Approach

Lassoued

Abderrahim

2014

Complex System Modelling and Control Through Intelligent Soft Computations

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“…(13)) is used to predict the future value of the glucose, and control action in each step is determined by minimizing the objective function given by Eq. (15). By increasing the weight factor r in this equation, the control action is damped but the control performance deteriorates.…”

Section: Conventional Model Predictive Controlmentioning

confidence: 99%

Model predictive control of blood sugar in patients with type‐1 diabetes

Najafabadi

Shahrokhi

2015

Optim Control Appl Methods

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Summary In this article, two adaptive model predictive controllers (AMPC) are applied to regulate the blood glucose in type 1 diabetic patients. The first controller is constructed based on a linear model, while the second one is designed by using a nonlinear Hammerstein model. The adaptive version of these control schemes is considered to make them more robust against model mismatches and external disturbances. The least squares method with forgetting factor is used to update the model parameters. For simulation study, two well‐known mathematical models namely, Puckett and Hovorka which describe the dynamical behavior of patient's body have been selected. The performances and robustness of the proposed controllers are tested for regulating the blood glucose of diabetic patients in presences of model mismatches and measurement noises. Simulation results indicate that the non‐linear model predictive controller (NMPC) outperforms the linear one. To improve the performance of the NMPC in rejecting the meal disturbances, two different feedforward control strategies have been considered. Simulation results indicate that the combined adaptive NMPC with feedforward controller has a better performance over the other considered control schemes. Copyright © 2015 John Wiley & Sons, Ltd.

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“…The adaptive technique with a fuzzy logic system has been investigated in this case study is controlling the pH neutralisation for set point tracking and load disturbance. Previously, Salehi (2009) had used fuzzy as an estimator to the adaptive model based control framework , Min (2006) work on adaptive algorithm of universal learning network, and Menzl et al (1996) had developed self-adapting mechanism inside fuzzy logic controller at neutralisation point. All of these work focus on utilising the fuzzy system for neutralisation control at nominal conditions with satisfactory performance.…”

Section: Introductionmentioning

confidence: 99%