Du Yihao, Wang Hao, Qiu Shi, Yao Wenxuan, Xie Ping, Chen Xiaoling
Key Lab of Measurement Technology and Instrumentation of Hebei Province Institute of Electric Engineering, Yanshan University, Qinhuangdao, China.
Front Robot AI. 2018 Oct 8;5:116. doi: 10.3389/frobt.2018.00116. eCollection 2018.
Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in the flexibility and stability of the control system. Therefore, we proposed an advanced adaptive control method for lower limb rehabilitation robot. The method was devised with a dual closed loop control strategy based on the surface electromyography (sEMG) and plantar pressure to improve the robustness of the adaptive control for the rehabilitation robots. First, in the outer loop control, an advanced variable impedance controller based on the sEMG and plantar pressure was designed to correct robot's reference trajectory. Then, in the inner loop control, a sliding mode iterative learning controller (SMILC) based on the variable boundary saturation function was designed to achieve the tracking of the reference trajectory. The experiment results showed that, in the designed dual closed loop control strategy, a variable impedance controller can effectively reduce trajectory tracking errors and adaptively modify the reference trajectory synchronizing with the motion intention of patients; the designed sliding mode iterative learning controller can effectively reduce chattering in sliding mode control and excellently achieve the tracking of rehabilitation robot's reference trajectory. This study can improve the performance of the human-robot interaction of the rehabilitation robot system, and expand the application to the rehabilitation field.
康复机器人在康复领域发挥着重要作用，有效的人机交互有助于推动康复机器人的发展。尽管已经开展了许多关于人机交互的研究，但控制系统的灵活性和稳定性仍存在一些局限性。因此，我们提出了一种用于下肢康复机器人的先进自适应控制方法。该方法采用基于表面肌电图（sEMG）和足底压力的双闭环控制策略来提高康复机器人自适应控制的鲁棒性。首先，在外环控制中，设计了一种基于sEMG和足底压力的先进可变阻抗控制器来校正机器人的参考轨迹。然后，在内环控制中，设计了一种基于可变边界饱和函数的滑模迭代学习控制器（SMILC）来实现对参考轨迹的跟踪。实验结果表明，在所设计的双闭环控制策略中，可变阻抗控制器能够有效降低轨迹跟踪误差，并根据患者的运动意图自适应地修改参考轨迹；所设计的滑模迭代学习控制器能够有效减少滑模控制中的抖振，并出色地实现康复机器人参考轨迹的跟踪。本研究能够提高康复机器人系统人机交互的性能，并拓展其在康复领域的应用。
