DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Abstract

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.

Genetic Optimization based Adaptive Fuzzy Logic Control of a pH Neutralization Process

“…One of the approaches proposed use of material balances and electroneutrality relations on component ions for dynamic pH process model and time-optimal pH control [4,5]. Because of its simplicity, this model has been used by many researchers as a platform to introduce AI based control techniques [6][7][8]. In addition, dynamic pH models based on reaction invariant and strong acid equivalent were designed for various nonlinear and adaptive control schemes [9][10][11][12][13].…”

Online Identification and Control of pH in a Neutralization System

“…Babuska et al implemented a fuzzy self-tuning PI controller for a small-scale fermentation system successfully. Venkateswarlu and Anuradha formulated a dynamic fuzzy adaptive controller (DFAC) and found that DFAC provides improved performance for the control of highly nonlinear pH processes compared to conventional PI, fuzzy, and adaptive PID controllers.…”