Design and tuning of standard additive model based fuzzy PID controllers for multivariable process systems

Area of Science:

• Control Engineering
• Automation Systems
• Fuzzy Logic Systems

Background:

• Multivariable processes require sophisticated control strategies.
• Existing fuzzy proportional-integral-derivative (FPID) controllers may lack robust tuning methods for complex systems.
• Standard additive model inference in fuzzy systems presents opportunities for enhanced control.

Purpose of the Study:

• To design and present a novel two-level tuning method for FPID controllers.
• To ensure closed-loop stability for n x n multi-input-multi-output (MIMO) processes.
• To evaluate the performance of two FPID configurations on a real-time multizone temperature control problem.

Main Methods:

• A two-level tuning scheme involving low-level (linear gains) and high-level (fuzzy output nonlinearity) adjustments.
• Utilizing the standard additive model for fuzzy inference.
• Implementation and testing on a 3 x 3 real-time multizone temperature control system.

Main Results:

• The proposed two-level tuning method effectively controls multivariable processes.
• Closed-loop stability is guaranteed for the considered n x n MIMO systems.
• Performance evaluation demonstrates the efficacy of the FPID configurations in a practical temperature control scenario.

Conclusions:

• The developed FPID controller design and tuning method offer a stable and effective solution for multivariable process control.
• The two-level tuning approach provides a systematic way to optimize controller performance.
• The study validates the practical applicability of the proposed method in real-time industrial settings.