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This study introduces global sensitivity-based input shapers for 3D Unmanned Aerial Vehicle (UAV) systems, focusing on enhancing precision motion control amid uncertainties in payload mass and rope length. By integrating the Shapley value concept into controller design, the authors systematically mitigate the controller's sensitivity to unknown parameters, leading to improved robustness. Numerical simulations reveal that the proposed Shapley-based input shapers significantly outperform traditional non-robust and robust designs, establishing a new framework for uncertainty-aware control in aerial payload transport.
Shapley-based input shapers can drastically enhance UAV payload control, reducing sensitivity to uncertainties by leveraging game theory principles.
This work presents a comprehensive analysis and design of global sensitivity-based input shapers for a 3D Unmanned Aerial Vehicle-payload system, emphasizing robustness against uncertainties in payload mass and rope length. The proposed approach also leverages the Shapley value concept in controller design to systematically account for uncertainties, thereby reducing the controller's sensitivity to unknown parameters. To validate the effectiveness of the methodology, numerical simulations are conducted, comparing the proposed controller against non-robust, robust, and minimax designs. The results demonstrate that the standard global sensitivity or Shapley-based input shapers improve performance and offer a promising framework for uncertainty-aware control in aerial payload transport.