An Adaptive Neuro-Fuzzy Approach to Control a Distillation Column

Abstract

In this paper we use a control strategy that enhances a fuzzy controller with self-learning capability for achieving the control of a binary methanol-propanol distillation column. An Adaptive-Network-based Fuzzy Inference System (ANFIS) architecture extended to cope with multivariable systems has been used. This allows the tuning of parameters both of the membership functions and the consequents in a Sugeno-type inference system. To satisfy the control objectives the backpropagation gradient descent through the plant method is applied, hence identification of the plant dynamics is also needed. The performance of the resulting neuro-fuzzy controller under different reference settings for the concentration of methanol demonstrates the stabilisation of the concentration profiles in the column, leading to an effective methanol composition control.

Power Balance Control for Two Motors Driving Shearer Base on ANFIS

“…Since introduction, ANFIS networks have been successfully applied to classification tasks, rule-based process controls, pattern recognition problems and the like. Here a fuzzy inference system comprises of the fuzzy model proposed by Takagi and Sugeno to formalize a systematic approach to generate fuzzy rules from an input output data set [4] . ANFIS Structure [2] [4] [5] [6] The corresponding equivalent ANFIS structure is shown in Fig.…”

Hierarchical neuro-fuzzy current control for a shunt active power filter

“…ANFIS is also suitable for fault diagnosis of electric machinery when trained using various healthy and faulty case studies as presented in [14][15][16]. ANFIS based controllers for some other nonlinear process control purposes are presented in [17][18]. However application of ANFIS based controllers and hierarchical neuro-fuzzy control is not reported for any custom power device like active power filters and any other power electronic device in the literature.…”

Development of the Feed Part Control System for Rectification Process

“…For a long time, the control of the rectifying column has been one of the research hot spots. Several control strategies are proposed, such as the fuzzy controller with self-learning capability for achieving the control of a binary methanol-propanol rectifying column [2].…”