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

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

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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.

Inspec keywords: nonlinear control systems; pH control; control system synthesis; model reference adaptive control systems; fuzzy control

Other keywords: fuzzy relational model-based control; nonlinear controllers; fuzzy logic; continuously stirred tank reactor; model predictive controller; identification; deadtime; CSTR; pH control

Subjects: Nonlinear control systems; Chemical variables control; Control system analysis and synthesis methods; Self-adjusting control systems; Fuzzy control; Formal logic

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