Performance Analysis of Hybrid AI and Fuzzy Controllers in Autonomous Robotic Systems
Bachu Pradeep Kumar, P Pravallika Chandar, R Rechal
This research presents a comprehensive performance analysis of hybrid AI and fuzzy logic controllers
in autonomous robotic systems. The study compares traditional controllers, fuzzy logic-based systems,
and hybrid AI controllers across key performance metrics, including accuracy, responsiveness, and
robustness. The experimental results demonstrate that hybrid AI controllers significantly outperform
their counterparts, achieving an accuracy of 92.3%, faster responsiveness at 95 milliseconds, and robust
adaptability with an 89.7% success rate in uncertain environments. Fuzzy logic controllers also show
notable improvements over traditional controllers, particularly in handling uncertainty and improving
decision-making. These findings underscore the potential of integrating hybrid AI with fuzzy logic to
enhance the overall efficiency and reliability of autonomous robotic systems in dynamic, real-world
environments.
