School of Electrical Engineering, Yanshan University, Qinhuangdao 066012, China.
Sensors (Basel). 2023 Jun 3;23(11):5311. doi: 10.3390/s23115311.
The restricted posture and unrestricted compliance brought by the controller during human-exoskeleton interaction (HEI) can cause patients to lose balance or even fall. In this article, a self-coordinated velocity vector (SCVV) double-layer controller with balance-guiding ability was developed for a lower-limb rehabilitation exoskeleton robot (LLRER). In the outer loop, an adaptive trajectory generator that follows the gait cycle was devised to generate a harmonious hip-knee reference trajectory on the non-time-varying (NTV) phase space. In the inner loop, velocity control was adopted. By searching the minimum L2 norm between the reference phase trajectory and the current configuration, the desired velocity vectors in which encouraged and corrected effects can be self-coordinated according to the L2 norm were obtained. In addition, the controller was simulated using an electromechanical coupling model, and relevant experiments were carried out with a self-developed exoskeleton device. Both simulations and experiments validated the effectiveness of the controller.
在人机骨骼交互（HEI）过程中，控制器带来的受限姿势和非受限顺应性可能导致患者失去平衡甚至摔倒。在本文中，为下肢康复外骨骼机器人（LLRER）开发了一种具有平衡引导能力的自协调速度矢量（SCVV）双层控制器。在外环中，设计了一个遵循步态周期的自适应轨迹生成器，以便在非时变（NTV）相空间中生成协调的髋关节-膝关节参考轨迹。在内环中，采用了速度控制。通过搜索参考相轨迹和当前配置之间的最小 L2 范数，获得了期望的速度矢量，这些速度矢量可以根据 L2 范数进行自我协调，从而实现激励和修正效果。此外，还使用机电耦合模型对控制器进行了模拟，并使用自行开发的外骨骼设备进行了相关实验。模拟和实验均验证了控制器的有效性。
