Modeling, simulation and experimental realization of a new nonlinear fuzzy PID controller using Center of Gravity...

Area of Science:

• Fuzzy Systems Engineering
• Control Theory
• Computational Intelligence

Background:

• Center of Gravity (CoG) defuzzification, while established, is computationally complex, limiting its use in fuzzy controller modeling.
• Modeling fuzzy Proportional Integral Derivative (FPID) controllers, especially with CoG defuzzification and a two-dimensional input space, is rarely documented in existing literature.

Purpose of the Study:

• To model and design a novel FPID controller utilizing a two-dimensional input space and CoG defuzzification.
• To develop a method for combining incremental FPI and FPD controller outputs to achieve the FPID control effort.

Main Methods:

• The study employs a two-dimensional input space, CoG defuzzification, Min t-norm, Max t-conorm, and Larsen Product (LP) inference.
• Incremental FPI and FPD controller structures were derived using these methods.
• The new FPID controller's incremental control effort is generated by summing the individual FPI and FPD control efforts.

Main Results:

• A novel mathematical model for an FPID controller was successfully developed using CoG defuzzification and a two-dimensional input space.
• The proposed FPID controller effectively integrates incremental FPI and FPD control actions.
• Simulation and real-time experiments validated the controller's applicability and usefulness.

Conclusions:

• The developed FPID controller offers a viable solution for applications requiring CoG defuzzification despite its computational complexity.
• This research addresses a gap in the literature by providing a model for FPID controllers using both two-dimensional input space and CoG defuzzification.
• The findings demonstrate the practical utility of the novel FPID controller through empirical validation.