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

Christoph Sing, Bruce Postlethwaite

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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 non-linear 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 (MPC), 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 MPC. 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.
Original languageEnglish
Pages (from-to)263-268
Number of pages5
JournalIEE Proceedings Control Theory and Applications
Issue number3
Publication statusPublished - May 1997


  • control system synthesis
  • fuzzy control
  • model reference
  • adaptive control systems
  • nonlinear control systems
  • pH control


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