Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process

Abstract

-This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.

Intelligent Control of an Industrial Debutanizer Column

“…Thus, the results indicated that the ANN is unable to properly extrapolate the process behavior in regions where the operating data points are not evenly distributed. In contrast, the ANFIS shows overall better capability of modeling, adaptation, and generalization [52].…”

Evolutionary algorithms based tuning of PID controller for an AVR system

“…The conventional method for tuning of PID controllers such as Ziegler-Nichols (Z-N) and Cohen-Coon techniques are producing only stable tuned parameters with some oscillation and overshoot output response. In order to avoid the shortcomings of the conventional tuning methods, soft computing techniques like Artificial Neural Network and Fuzzy logic approaches have been proposed in the literature [5,6]. Evolutionary algorithms-based approaches are also proposed to tune the parameters of PID controller in many applications in literature.…”

Identificação de Sistemas Dinâmicos Não Lineares Utilizando Modelos Neuro-Fuzzy Lineares Locais com um Algoritmo LOLIMOT-PSO

“…Desde sua concepção, o algoritmo sempre chamou atenção por sua construção intuitiva e vem sendo aplicado em diversos trabalhos. Em Petchinathan et al (2014) esta técnica foi comparada ao ANFIS (Adaptive Neuro-Fuzzy Inference System) para modelagem de um experimento envolvendo neutralização de pH, apesar de em termos gerais ambas se mostrarem válidas para modelagem de sistemas em tempo real complexos e não lineares, LOLIMOT apre-sentou resultados melhores, precisando de menor tempo de treinamento e apresentando erro quadrático médio menor. Schaffnit et al (2000) evidenciam a facilidade de controle da complexidade do algoritmo e a boa interpretabilidade dos resultados ao utiliza-lo para obter o modelo de um turbo compressor com turbina de geometria variável.…”