基于折纸启发气动执行器的膝关节康复软外骨骼设计与Koopman模型预测控制
Koopman Model Predictive Control of An Origami-Inspired Soft Exoskeleton for Knee Rehabilitation
AI总结：
针对软外骨骼非线性人机交互控制难题，提出基于折纸气动执行器的膝关节康复外骨骼，利用Deep Koopman Network结合EMG信号建模并以MPC实现实时控制，在被动和主动训练中均优于传统PID。
摘要：
有效的康复方法对于中风引起的下肢功能障碍的恢复至关重要。如今，机器人外骨骼在康复领域展现出了巨大的潜力。然而，传统的刚性外骨骼通常较为笨重，且需要花费大量精力帮助患者穿戴。此外，它还需要额外的柔顺控制来保证安全。相比之下，软外骨骼穿戴轻松舒适且具有内在柔顺性，但其复杂的非线性人机交互动力学给控制带来了重大挑战。在本研究中，基于折纸启发的气动执行器，我们设计了一款穿戴轻松舒适的膝关节康复外骨骼。为了保证控制性能并实现良好的人机交互，我们首先使用Deep Koopman Network对人机交互动力学进行建模。具体而言，通过将肌电图（EMG）信号以及控制气动机器人阀门和泵的PWM波占空比作为输入，线性Koopman模型准确地捕捉了复杂的人机交互动力学。接下来，基于获得的Koopman模型，我们进一步使用模型预测控制（MPC）来控制软体机器人，并实时帮助用户进行康复训练。康复训练的目标是跟踪屏幕上显示的给定参考信号。实验表明，通过将EMG信号整合到Koopman模型中，我们在很大程度上提高了模型精度。此外，从个体自身数据训练出的个性化Koopman模型表现优于非个性化模型。因此，我们的控制框架在被动和主动训练模式下均优于传统的PID控制。因此，所提出的方法为软体康复机器人提供了一种新的控制框架。
英文摘要：
Knee rehabilitation plays a critical role in restoring patients' mobility and functional independence. Traditional rigid rehabilitation exoskeletons are often bulky and cumbersome to wear, whereas soft pneumatic exoskeletons offer lightweight, wearable, and intrinsically compliant solutions that are better suited for human--robot interaction. However, achieving precise motion control for soft exoskeletons remains challenging due to the difficulty of accurately modeling pneumatic actuators and the patient-specific human--robot coupled dynamics during rehabilitation training. To address these challenges, this paper proposes a Koopman-based Model Predictive Control (KMPC) framework for soft knee rehabilitation exoskeletons. The nonlinear human--robot coupled system is represented through a lifted linear Koopman model, enabling predictive control with explicit handling of constraints. In addition to actuation commands used to control valves and pumps, electromyography (EMG) signals are incorporated as system inputs, allowing the Koopman model to capture voluntary neuromuscular contribution and individual neuromuscular characteristics. Experimental results on both healthy participants and patients demonstrate that the proposed framework improves model prediction accuracy and effectively captures subject-specific behaviors, thereby supporting EMG-informed subject-specific assistance within the tested seated knee-rehabilitation setting. Compared with conventional Proportional--Integral--Derivative (PID) control, the proposed KMPC approach achieves lower tracking errors and reduced actuation effort in both passive and active rehabilitation modes. Additional comparisons with Iterative Learning Control (ILC) further validate the tracking performance of the proposed controller.
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