| Control of a pH process at the titration end point is the subject of this thesis. With the inclusion of the process dead time, the pH process becomes highly nonlinear and difficult to control. We developed an Accelerated Internal Model Control algorithm in this thesis, which is the best controller for the pH system with dead time. In this control algorithm, a smaller reactor volume is used in the internal model and this can make the controller work better and faster than the IMC and AIMC algorithms. Experimental results also demonstrate this conclusion. We also developed a Genetic Algorithms Model Predictive Control algorithm (GAMPC), which is useful when the process model is very complex and cannot be mathematically inverted. |
