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<p>This paper employs the Newton-Euler method to enhance the modeling process of 6-UPS parallel robots, overcoming existing limitations. And this paper introduces a trajectory tracking control scheme integrating RBFNN and UNDO. The improved dynamics model facilitates advanced control strategies by being both expeditious and precise. Utilizing RBFNN, the control scheme reduces disparities between theoretical and actual systems, while UNDO accurately compensates for external force disturbances. Simulation experiments confirm the effectiveness of the proposed model, showing a significant speed improvement over traditional models. Additionally, the improved model demonstrates lower convergence rates and error indexes (MAE and RMSE) compared to traditional approaches. Experimental results further validate the feasibility and superiority of the trajectory tracking control strategy that combines RBFNN and UNDO, showcasing remarkable error index optimizations (MAE and RMSE) compared to using RBFNN control alone. At the end, this paper further explores the methodology for selecting the observation matrix and the robustness of coping with varying levels of external disturbances under the same observation matrix parameters. The experimental results indicate that selecting parameters for the observation matrix of the 6-UPS parallel mechanism is relatively straightforward, making it suitable for trajectory tracking control of the 6-UPS parallel mechanism and capable of handling complex unknown external force disturbances.</p>

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Trajectory tracking control of 6-UPS type parallel robots combining RBFNN and UNDO

  • 期刊论文

  • 发表日期：

    2025年2月3日

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  • Chengcheng Liu,
  • Jiayi Wen,
  • Peiyi Zhu

摘要

This paper employs the Newton-Euler method to enhance the modeling process of 6-UPS parallel robots, overcoming existing limitations. And this paper introduces a trajectory tracking control scheme integrating RBFNN and UNDO. The improved dynamics model facilitates advanced control strategies by being both expeditious and precise. Utilizing RBFNN, the control scheme reduces disparities between theoretical and actual systems, while UNDO accurately compensates for external force disturbances. Simulation experiments confirm the effectiveness of the proposed model, showing a significant speed improvement over traditional models. Additionally, the improved model demonstrates lower convergence rates and error indexes (MAE and RMSE) compared to traditional approaches. Experimental results further validate the feasibility and superiority of the trajectory tracking control strategy that combines RBFNN and UNDO, showcasing remarkable error index optimizations (MAE and RMSE) compared to using RBFNN control alone. At the end, this paper further explores the methodology for selecting the observation matrix and the robustness of coping with varying levels of external disturbances under the same observation matrix parameters. The experimental results indicate that selecting parameters for the observation matrix of the 6-UPS parallel mechanism is relatively straightforward, making it suitable for trajectory tracking control of the 6-UPS parallel mechanism and capable of handling complex unknown external force disturbances.