Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control

This book presents innovative methods combining fuzzy logic with differential evolution algorithms to tackle complex control challenges effectively.

In Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control, the authors delve into the intricate realms of fuzzy logic and bio-inspired algorithms, particularly emphasizing the differential evolution algorithm. This exploration is not merely theoretical; it aims to bridge these two fields to address various control challenges effectively. By integrating fuzzy control concepts, the book presents a novel approach that enhances the performance of control systems under uncertain conditions.

The core premise of the book is that the synergy between fuzzy systems and the differential evolution algorithm can lead to superior solutions for complex control problems. To substantiate this claim, the authors conduct rigorous testing using five benchmark control scenarios, including water tank management, temperature control, mobile robot navigation, and inverted pendulum stabilization. Each scenario is meticulously analyzed using both Type-1 and Interval Type-2 fuzzy systems, showcasing the versatility and adaptability of the proposed methods.

The final control problem examined involves a D.C. motor, where experiments are conducted with various fuzzy systems, including Type-1, Interval Type-2, and Generalized Type-2 configurations. The findings reveal that the combination of fuzzy systems and the differential evolution algorithm consistently yields improved results, particularly as uncertainty increases. Thus, Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control serves as a significant contribution to the field, offering insights into enhancing fuzzy controllers for complex, real-world applications.