基于分数阶导纳与逆动力学鲁棒控制的并联机器人人机协作

Human-robot Collaboration of Parallel Robots Based on Fractional Admittance and Inverse Dynamic Robust Control

  • 摘要: 鉴于传统的串联机器人承载能力有限、精度不高,并联机器人的闭环结构增加了其动力学解算难度且响应速度较慢,都没法满足人机协作的要求,本文提出了一种分数阶导纳控制算法用于实现人机协作,同时提升控制系统的响应性能。设计了逆动力学鲁棒控制,以实现对未知交互力的鲁棒性。将所提的控制算法应用于经典的Stewart并联平台,并对并联平台的响应性能以及跟踪性能进行了多次实验与评估。结果表明,所述的方法使Stewart并联平台在负载任务最重的Z轴平移自由度上对未知交互力的响应速度平均提升51.16%,同时跟踪误差峰值平均降低了50.83%。

     

    Abstract: The traditional tandem robots are of limited carrying capacity and low accuracy, and the closed-loop structure of parallel robots increases the difficulty in solving dynamics and has a slow response speed. Both structures can't meet the requirements of human-robot collaboration. This paper proposes a fractional-order admittance control algorithm to address these issues to improve response performance while enabling human-robot collaboration. An inverse dynamics robust control algorithm is also designed to ensure robustness against unknown interaction forces. The proposed control algorithm is applied to a classical Stewart parallel platform, and its response and tracking performance are evaluated through experiments. The results show that the described method resulted in an average 51.16% increase in the response speed of the Stewart parallel platform to unknown interaction forces in the Z-axis translational degree of freedom, where the loading task is heaviest, as well as an average reduction in the peak tracking error of 50.83%.

     

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