Nascimento Jean Mendes, Taira Camila, Becman Eric Cito, Forner-Cordero Arturo
Biomechatronics Laboratory, Escola Politécnica, University of Sao Paulo, São Carlos 13566-590, SP, Brazil.
Biomimetics (Basel). 2025 Feb 25;10(3):138. doi: 10.3390/biomimetics10030138.
Control remains a challenge in precision applications in robotics, particularly when combined with execution in small time intervals. This study employed a two-degree-of-freedom (2-DoF) planar robotic arm driven by a detailed human musculoskeletal model for actuation, incorporating nonlinear control techniques to execute a precision task through simulation. Then, we compared these simulations with real experimental data from healthy subjects performing the same task. Our results show that the Feedback Linearization Control (FLC) applied performed satisfactorily within the task execution constraints compared to a robust nonlinear control technique, i.e., Sliding Mode Control (SMC). On the other hand, differences can be observed between the behavior of the simulated model and the real experimental data, where discrepancies in terms of errors were found. The model errors increased with the amplitude and remained unchanged with any increase in the task execution frequency. However, in human trials, the errors increased both with the amplitude and, notably, with a drastic rise in frequency.
在机器人技术的精密应用中，控制仍然是一项挑战，尤其是当与在短时间间隔内执行相结合时。本研究采用了一种由详细的人体肌肉骨骼模型驱动的两自由度（2-DoF）平面机器人手臂进行驱动，纳入非线性控制技术以通过模拟执行精密任务。然后，我们将这些模拟与健康受试者执行相同任务的真实实验数据进行了比较。我们的结果表明，与一种强大的非线性控制技术，即滑模控制（SMC）相比，所应用的反馈线性化控制（FLC）在任务执行约束内表现令人满意。另一方面，可以观察到模拟模型的行为与真实实验数据之间存在差异，其中在误差方面发现了差异。模型误差随振幅增加而增加，并且随着任务执行频率的任何增加而保持不变。然而，在人体试验中，误差既随振幅增加，而且值得注意的是，随着频率的急剧上升而增加。
