Bioinspired optimization on controlled anthropomorphic manipulator robots

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The proposed framework boosts tracking precision in anthropomorphic manipulator robots, ensuring effective motion control.
Simulation studies show reduced control effort and improved convergence dynamics compared to classical approaches.
Integration of bacteria-inspired algorithms and artificial neural networks enhances the adaptability of control gains.
The methodology employs robust control laws to navigate structural uncertainty and dynamic perturbations without relying on full dynamic models.

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This research demonstrates improved motion control and adaptability in robotic systems, highlighting bioinspired optimization methods.

Galvan-Perez et al. (2025) studied this question.

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