Authors - Prashant Gaidhane Abstract - The control of robotic arms presents signicant engineering challenges due to their multi-input multi-output characteristics, strong coupling e ects, and inherent nonlinearities. The optimization landscape for controller parameter tuning exhibits multiple local optima, complicating the search for globally optimal solutions. Achieving precise end e ector path prole following in robotic systems demands sophisticated control methodologies tailored to handle these complexities. This re- search introduces an innovative cooperative foraging-based Grey Wolf Optimizer (CFGWO) algorithm to address these control challenges. The proposed methodology employs CFGWO to optimize the parameters of a PI D-based fuzzy regulator, targeting enhanced end e ector path prole performance in a Planar dual-link robotic arm with terminal load. The PI D-based fuzzy regulator incorporates additional design parameters beyond conventional PID structures, o ering expanded exibility in controller synthesis. The optimization performance of CFGWO is bench- marked against established algorithms including standard GWO, GWO- ABC hybrid, and LGWO variants. Performance evaluation focuses on minimizing the Integral of Time-weighted Absolute Error (ITAE) criterion. Results indicate that CFGWO achieves superior optimization con- vergence rates and delivers the lowest ITAE values among tested algorithms. Comprehensive experimental validation and performance analysis conrm the enhanced e ectiveness of the CFGWO approach, demonstrating its capability to balance exploration and exploitation mechanisms for robust global optimization in engineering applications.
