pH control: handling nonlinearity and deadtime with fuzzy relational model-based control

The application of fuzzy logic to the design of nonlinear controllers has become increasingly popular in recent years. Most of the developments have been in controllers of the rule-based type. An alternative approach, and one which reflects trends in conventional control, is to use fuzzy logic to build a process model, and then to incorporate this into a standard model-based controller scheme. The paper proposes the application of fuzzy relational models (FRMs) for the nonlinear control of a pH process. The pH in both a simulated and a laboratory continuously stirred tank reactor (CSTR) was controlled by a model predictive controller (MPG), incorporating a fuzzy model created using a recently developed method of FRM identification. The controller performance is compared with that of a fuzzy rule-based controller, that of a PID controller and that of a linear MPG. The comparison shows the superiority of fuzzy relational model-based control (FRMBC) for highly nonlinear processes. The suitability of the FRMBC for real-world applications is demonstrated by its control performance on a laboratory-scale plant