Dynamic fuzzy adaptive controller for pH

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.