Improved fuzzy PID controller design using predictive functional control structure.

Area of Science:

• Control Engineering
• Industrial Process Control
• Artificial Intelligence in Engineering

Background:

• Conventional PID controllers exhibit limitations in performance under uncertainty due to restricted degrees of freedom.
• Existing fuzzy PID controllers may not fully leverage predictive capabilities for optimal control law derivation.

Purpose of the Study:

• To introduce a novel PID control strategy integrating fuzzy logic and Predictive Functional Control (PFC).
• To enhance ensemble control performance in systems with inherent uncertainties, specifically demonstrated on a coke furnace temperature model.

Main Methods:

• The proposed method utilizes the Predictive Functional Control (PFC) framework to predict future process behavior based on current input signals.
• A multi-step prediction approach is employed within a fuzzy PID control structure to determine the optimal control law.
• The controller's effectiveness is validated using a temperature model of a coke furnace.

Main Results:

• The novel fuzzy PID control scheme demonstrates superior performance compared to conventional PID control.
• The proposed method outperforms fuzzy self-adaptive PID control in the tested coke furnace temperature model.
• The integration of PFC and fuzzy logic effectively addresses uncertainties and improves control accuracy.

Conclusions:

• The presented fuzzy PID control method, enhanced by PFC, offers a robust solution for industrial temperature control challenges.
• This approach effectively overcomes the limitations of conventional PID controllers in uncertain environments.
• The study validates the practical applicability and effectiveness of the proposed control strategy in complex industrial settings.