This paper introduces a novel self-adaptive shuffling mechanism within the Shuffled Frog Leaping Algorithm (SFLA) to improve its efficacy in the tuning of fuzzy logic Proportional-Derivative with Gravity Compensation (PD+G) controllers for trajectory tracking in the UP6 robotic manipulator. The proposed mechanism improves the balance between exploration and exploitation by continually modifying the frequency and intensity of scrambling in accordance with population diversity. This adaptive approach overcomes the constraints of the conventional SFLA, which implements a static scrambling process by facilitating more efficient global search and local refinement. The fuzzy controller parameters for a 6-DOF robotic manipulator are optimized using the enhanced SFLA to guarantee precise trajectory tracking. The self-adaptive shuffling mechanism results in enhanced tracking accuracy and faster convergence in comparison to the standard SFLA, as evidenced by the simulation results. The results of this study suggest that the proposed method is a plausible solution for real-time control applications that necessitate efficient parameter tuning in nonlinear systems.

Optimization SFLA Fuzzy PD+G controller Manipulator