Neural network based pH control of a weak acid &amp;#x2014; Strong base system

Tharakan, L. G.; Benny, Anish; Jaffar, N. E.; Jaleel, J. Abdul

doi:10.1109/imac4s.2013.6526494

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Many structures was used to describe the nonlinear behavior of the pH neutralization process: NARX model [11],Fuzzy neural network model [12], Neural networks [13], Wiener model [8], [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

Degachi¹,

Chagra²,

Ksouri³

2018

ijacsa

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Abstract-In this work, the pH neutralization process is described by a neural network Wiener (NNW) model. A nonlinear Model Predictive Control (NMPC) is established for the considered process. The main difficulty that can be encountered in NMPC is solving the optimization problem at each sampling time to determine an optimal solution in finite time. The aim of this paper is the use of global optimization method to solve the NMPC minimization problem. Therefore, we propose in this work, to use the Self Organizing Migrating Algorithm (SOMA) to solve the presented optimization problem. This algorithm proves its efficiency to determine the optimal control sequence with a lower computation time. Then the NMPC is compared to adaptive PID controller, where we propose to use the SOMA algorithm to formulate the PID in order to determine the optimal parameters of the PID. The performances of the two controllers based on the SOMA algorithm are tested on the pH neutralization process.

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