In context
In late 2024, human-robot collaboration was expanding beyond serial arms into heavier-duty parallel robots, which offer higher load capacity and precision but suffer from slow dynamic response and complex closed-loop dynamics. This study addressed a key gap: enabling safe, responsive physical interaction on a Stewart platform for tasks like large-part assembly.
What was reported
Researchers from Shenyang University of Technology and the Chinese Academy of Sciences proposed a fractional-order admittance controller combined with an inverse dynamics robust controller for a Stewart parallel platform. The fractional-order approach replaces integer-order terms in classical admittance control, improving transient response and flexibility in shaping human-robot interaction dynamics.
To track the resulting compliant trajectories, the team designed an inverse dynamics controller with a nonlinear robust term, ensuring stability against unknown interaction forces and model uncertainties. Experiments on a 100 kg rated RX/YBT-6-100 Stewart platform showed that the method increased response speed to unknown forces by an average of 51.16% in the Z-axis (the heaviest loaded degree of freedom) and reduced peak tracking error by 50.83%.
The control scheme was implemented with a 5 ms sampling period, using LabVIEW for trajectory generation and a lower-level controller for driving the six electric cylinders.
Why it mattered
This work demonstrated that parallel robots can be made suitable for human-robot collaboration in industrial settings, overcoming their traditional slow response and coupling issues. The combination of fractional-order admittance and robust inverse dynamics offers a practical path to safer, more responsive heavy-duty collaborative automation, potentially expanding the use of parallel robots in assembly and machining tasks.
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%.
Source: 《机器人》期刊 (robot.sia.cn) · Published 2024-11-15 · “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”