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全球范围内由中风、脊髓损伤和物理伤害导致的手部功能障碍人数已超过5000万人。手部运动功能障碍使得患者失去生活自理能力，严重降低了患者的生活质量。然而，现有的手部辅助器具功能单一，且不能主动提供辅助力，因此研究手部助力外骨骼机器人协助患者完成对日常生活物品操作具有重要意义。基于此背景，本文围绕手部助力外骨骼机器人的系统设计、助力效果定量评估、被动人机交互控制、和主动人机交互控制展开研究。本文的主要工作与创新点归纳如下：

设计了一种能够辅助食指和拇指抓握的手部外骨骼，分析了机构的抓握灵巧性，保证抓握的有效性。依据文献统计，人手每天在不经意间会进行数千次抓握。为了保证手部外骨骼具有同样的抓握能力，本文设计了一款3个电机驱动的基于连杆传动的手部外骨骼。针对手指抓握特性，提出了一种基于冗余自由度的欠驱动机构，实现外骨骼对不同形状和大小物体的自适应抓握。建立了欠驱动机构的运动学和静力学，分析了该机构的抓握灵巧性，同时提出基于多目标遗传算法的外骨骼抓握力优化方法，保证抓握稳定性。最后对手部外骨骼样机进行了运动透明性和抓握灵巧性实验。实验结果表明该外骨骼可以帮助穿戴者抓握不同物体，具有抓握灵巧性。

提出了一种基于人体运动、人体生理和人体感受的手部助力外骨骼有效性评估框架，实现了手部助力外骨骼功能的定量评估。评估一款手部外骨骼功能的有效性对于手部外骨骼改进和用户使用都具有意义。本文提出从人体运动、生理、感受三个层面建立手部外骨骼有效性定量评估方法，设计了5个指标来定量评估所设计的外骨骼。同时，构建了手部助力外骨骼功能验证的实验范式。最后通过实验证明所提出的助力外骨骼有效性评估框架的可行性，同时证明了所设计的手部助力外骨骼能够贴近人手自然抓握动作和降低穿戴者用力和肌肉疲劳度。

提出了一种基于降维扩张状态观测器的手部外骨骼自抗扰控制和基于导纳控制的柔顺控制混合方法，保证人机交互安全。本文针对人手外骨骼耦合系统动力学建模难问题以及传统的PID控制抗扰性能差问题，设计了基于扩张状态观测器的自抗扰控制器实现对助力外骨骼的位置控制，分析控制器稳瞬态性能，以及证明了该控制器的有界输入有界输出稳定性。同时，也设计了基于降阶扩张状态观测器的自抗扰控制器，分析了两种控制器的内在联系。最后比较了两种控制器的抗扰性能和抵抗噪声的能力。此外，本文设计基于导纳控制的手部外骨骼控制器，保证了人机交互的柔顺性。最后通过实验验证了所提控制方法的抗扰性能和柔顺性能。

提出了一种基于sEMG驱动肌肉骨骼模型的主动控制方法，实现用户对外骨骼在动态抓握任务场景下的主动控制。针对当前手部外骨骼辅助策略个性化差问题，本文首先建立基于Hill模型的神经肌肉骨骼模型，通过实验标定得到个性化肌肉骨骼模型。本文提出基于sEMG驱动个性化肌肉骨骼模型的受试者手指关节扭矩估计方法，然后设计了基于导纳控制的外骨骼控制器，实现了用户对外骨骼的主动控制辅助其抓握。主动控制方法通过评估用户的自主努力程度来调整外骨骼辅助程度。最后通过实验验证辅助策略的个性化和主动控制方法的有效性。

More than 50 million people worldwide have permanent hand injuries due to stroke, spinal cord injury, and physical injury. Hand motor dysfunction makes patients lose their ability to take care of themselves and seriously reduces their quality of life. However, the existing hand assistive devices have a single function and cannot actively provide assistive force. Therefore, it is of great significance to study hand-assisted exoskeleton robots to assist patients in completing operations on daily objects. Based on this background, this paper focuses on the system design of the hand-assisted exoskeleton robot, the quantitative evaluation of the assistance effect, the passive human-robot interaction control, and the active human-robot interaction control. The main work and innovations of this paper are summarized as follows: 

A hand exoskeleton capable of assisting index finger and thumb grasping is designed, and the grasping dexterity of the mechanism is analyzed, and guarantees the effectiveness of the grip. According to literature statistics, the human hand inadvertently makes thousands of grasps daily. To ensure that the hand exoskeleton has the same grasping ability, a three-motor-driven hand exoskeleton based on link transmission is designed in this paper. Aiming at the grasping characteristics of fingers, an underactuated mechanism based on redundant degrees of freedom is proposed to realize the adaptive grasping of objects of different shapes and sizes by the exoskeleton. The kinematics and statics of the underactuated mechanism are established, the grasping dexterity of the mechanism is analyzed, and an optimization method of exoskeleton grasping force based on a multi-objective genetic algorithm is proposed to ensure the grasping stability. Finally, the motion transparency and grasping dexterity experiments are carried out on the hand exoskeleton prototype. The experimental results show that the proposed exoskeleton can help the wearer to grasp different objects and has grasping dexterity.

A framework for evaluating the effectiveness of hand-assisted exoskeletons based on human motion, human physiology, and human feelings is proposed, which realizes the quantitative evaluation of the function of hand-assisted exoskeletons. Evaluating the effectiveness of a hand exoskeleton function has implications for both hand exoskeleton improvement and user usage. This paper proposes to establish a quantitative evaluation method for the effectiveness of hand exoskeletons from three levels of human movement, physiology and feelings, and designs five indicators to quantitatively evaluate the designed exoskeleton. At the same time, an experimental paradigm for the functional verification of the hand-assisted exoskeleton is constructed. Finally, the feasibility of the proposed framework for evaluating the effectiveness of the power-assisted exoskeleton is proved by experiments, and the designed hand-assisted exoskeleton can be close to the natural grasping motion of the human hand and reduce the wearer's force and muscle fatigue.

It is proposed that a hybrid method of active disturbance rejection control of hand exoskeleton based on reduced-order extended state observer and compliant control based on admittance control to ensure the safety of human-robot interaction. In this paper, aiming at the difficult problem of dynamic modeling of the coupled system of human hand exoskeleton and the problem of poor anti-disturbance performance of traditional PID control, an active disturbance rejection controller based on extended state observer is designed to realize the position control of the power-assisted exoskeleton, and the controller is analyzed. stable transient performance, and the bounded input bounded output stability of the controller is demonstrated. At the same time, the active disturbance rejection controller based on the reduced-order extended state observer is also designed, and the internal relationship between the two controllers is analyzed. Finally, the anti-disturbance performance and anti-noise ability of the two controllers are compared. In addition, this paper also designs a hand exoskeleton controller based on admittance control, which ensures the flexibility of human-robot interaction. Finally, the anti-disturbance performance and compliance performance of the proposed control methods are verified by experiments.

A voluntary control method is proposed based on the sEMG-driven musculoskeletal model to realize the voluntary control of the user's exoskeleton in the dynamic grasping task scenario. Aiming at the problem of poor personalization of the current hand exoskeleton assistance strategy, this paper firstly establishes a neuromusculoskeletal model based on the Hill model, and obtains a personalized musculoskeletal model through experimental calibration. In this paper, a method for estimating finger joint torque of subjects based on sEMG-driven personalized musculoskeletal model is proposed, and then an exoskeleton controller based on admittance control is designed to realize the user's voluntary control of the exoskeleton to assist its grasping. Voluntary control methods adjust the degree of exoskeleton assistance by evaluating the user's voluntary effort. Finally, the effectiveness of the personalization and voluntary control method of the assistive strategy is verified by experiments.