手功能康复机器人系统若干关键技术研究
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摘要
随着偏瘫患者数量的逐年增加,患者运动功能的康复越来越多地引起人们的重视。运动功能的恢复对患者的日常生活能力影响很大,也是康复治疗中难以解决的问题之一,已成为现代康复医学和医疗工程的研究热点。康复机器人技术研究正是在这种需求形式下应运而生的一个崭新的研究领域,涉及到机器人学、康复医学、人体工程学、机械设计、控制理论及计算机科学等众多领域。
在国家863课题和自然科学基金的资助下,本文以自行研制的穿戴式手功能康复机器人为基础,对其本体机械结构及控制系统设计、驱动器的建模与控制、人手表面肌电信号的辨识等关键技术展开了深入的研究。本文主要的工作和贡献体现在以下几个方面：
分析和比较了现有的手功能康复设备,对人手的结构与功能、手指的运动约束进行了调查,并研究了具有代表性的食指生物力学模型。在此基础上研究了手功能康复机器人的机械结构设计。根据临床康复的需求,考虑穿戴的舒适性,安全性及运动的柔顺性,设计了针对手指多关节运动功能康复的机构。基本的结构采用连杆的外骨骼形式,驱动器选用柔顺性好的气动肌肉。搭建手功能康复机器人控制系统实验平台,并进行了基本的实验研究,验证了机构和控制系统的有效性。同时,还提出了机构改进的方案,为进一步设计真正临床实用化的手功能康复机器人奠定了良好的基础。
针对手功能康复机器人的驱动器——气动肌肉所组成的两种不同形式的驱动关节进行了分析和比较,说明气动肌肉扭力弹簧驱动关节是一种有效的驱动形式,非常适合于手功能康复机器人的应用。由于气动肌肉具有很强的时变和非线性特性,难以精确控制,因此,本文对气动肌肉的建模与控制进行了深入的研究。首先通过实验对气动肌肉的“三元素”模型进行了辨识,并在此基础上设计了滑模控制器,考虑到未建模的动力学以及系统存在的不确定的扰动,采用干扰观测器对这些不确定的非线性扰动进行观测,设计了基于非线性扰动观测器的滑模控制器,仿真和实验结果表明当系统存在不确定的建模误差和外部扰动的情况下,设计的控制方法可以保证系统跟踪性能要求。不同的制作工艺和材料特性会导致气动肌肉具有不同的动态特性,为了寻求更为通用和便捷的建模和控制方法,还采用一种新型的递归神经网络——回声神经网络对气动肌肉进行了建模,并基于回声神经网络设计了自适应控制器,通过仿真和实验进行了比较,说明了方法的有效性。
表面肌电信号反馈也是手功能康复机器人的一个重要组成部分,可用于康复评价和肌电控制策略中的意图检测,因此,本文也对人手表面肌电信号的辨识进行了较为深入的研究。采集人手做屈肘、伸肘、腕内旋、腕外旋、展拳及握拳六种不同动作时的表面肌电信号,利用离散小波分解提取特征值。针对特征值,采用支持向量机进行了分类,取得了很好的分类效果。通过与神经网络分类器的进一步比较可以发现,支持向量机对小规模样本的分类更加有效,具有同神经网络类似的优点,自动提取分类信息和自动特征选择,且具有更好的泛化能力,容易实施和控制,不会产生神经网络的局部极小和过学习的问题。
最后,对全文进行了总结,并指出了下一步需要进行的工作。
本博士论文通过深入研究手功能康复机器人系统的若干关键技术问题,为发展面向临床应用的手功能康复机器人系统提供必要的理论依据、实验数据和研究经验。随着研究工作的进一步深入和完善,手功能康复机器人将最终转化为康复产品,为更多的患者提供康复训练,提高他们的康复效果和生活质量,具有积极的学术和实际意义。
With the increasing number of the hemiplegia patients, attention has been drawn to the rehabilitation therapy of the movement functional disorder. Rehabilitation of the movement function has influenced on patient's daily life greatly. In nowadays, this topic is one of the most difficult problems in rehabilitation therapy and has become a hotspot of modern rehabilitation and medical studies. Rehabilitation robot research was born as a new study area due to the great demand in rehabilitation of the movement function. It involves robotics, rehabilitation medicine, ergonomics, mechanical design, control theory, computer science and other fields.
Under the support grants from National 863 project and National Natural Science Foundation, the research work in this dissertation focus on investigation some key technologies of wearable robot for rehabilitation of hand function. Our study includes mechanical and control system design of rehabilitation robot, modeling and control of pneumatic muscle actuator, identification of hand's surface electromyography (sEMG) signal, and so on. The main contributions of this dissertation focus on the following aspects:
First, the existing rehabilitation equipments for hand function are analyzed and compared. The structure, function and motion constraints of human hand are analyzed and biomechanical model of representative index finger is also investigated. Based on the investigations, the mechanical design of the hand rehabilitation robot is studied. Considering the needs of clinical rehabilitation, the wearing comfort, as well as safety and flexibility of motion, we design a mechanism which can assist fingers to complete multi-joints functional rehabilitation movements. The exoskeleton structure is adopted and the flexible PMs are selected as its actuator. A control system platform of the hand rehabilitation robot is built and the basic control experiments are also implemented to verify the effectiveness of the mechanical and control system. Furthermore, the improved mechnical design is also proposed, which lays a good foundation for realizing the clinical use of the hand rehabilitation robot.
Two different structural actuators consisting of the pneumatic muscle (PM) which is work as rehabilitation robot's actuator are analyzed and compared. The work indicates the proposed actuator configuration consisting of the PM and the torsion spring is an effective and promising method for rehabilitation robot application. The complex nonlinear dynamics and time-varying parameters of the PM make it difficult to control. Thus, the modeling and control of PM are also investigated in this dissertation. We identify three-element model of the PM by experiments. Based on this model, the sliding mode controller is designed. Considering the unmodeled dynamics and uncertain disturbance in the system, the disturbance observer is adopted to compensate for these uncertain nonlinear disturbances. The simulation and experimental results demonstrated that the model and the controller achieved the desired performance in tracking a desired trajectory within guaranteed accuracy regardless of modeling uncertainties and perturbation. The PM made by different workmanship and material has different dynamics. To get more convenient modeling and control method, a new type of recurrent neural networks-Echo Neural Network (ESN) is proposed to model the PMs. The adaptive controller is developed based on ESN. The effectiveness of the proposed method is validated by simulation and experiments.
The other important part of the rehabilitation robot is the sEMG signal feedback, which can be used to evaluate the effectiveness of the rehabilitation and recognize the motion intension. In order to fulfill the sEMG signal recognizing task, sEMG signal of elbow flexion, elbow extension, wrist pronation, wrist supination, palm extension and fist are collected. Then discrete wavelet transform is used to extract the signal's features. The Support Vector Machines (SVM) is used to classify these features. Compared with the neural network classifier, the experimental results show that SVM is more effective for small-scale sample classification. Besides having the advantages of neural network, which can extract the classification information and select features automatically, SVM also has better generalization ability and is easy to apply and control. Furthermore, it overcomes the disadvantages of artificial neural network such as overlearning and partially leading to minimum.
Finally, the summary of this dissertation and the directions of future work are presented.
Some key issues on hand rehabilitation robot system are investigated deeply in this thesis. These works provide the necessary theoretical basis, experimental data and valuable research experience for development of the clinic-oriented hand rehabilitation robot. With improvement of the further research work, some products on hand rehabilitation robot will be realized. These products can provide more patients effective rehabilitation training and improve the quality of rehabilitation. This is a research work of positive academic and practical significance.
在国家863课题和自然科学基金的资助下,本文以自行研制的穿戴式手功能康复机器人为基础,对其本体机械结构及控制系统设计、驱动器的建模与控制、人手表面肌电信号的辨识等关键技术展开了深入的研究。本文主要的工作和贡献体现在以下几个方面：
分析和比较了现有的手功能康复设备,对人手的结构与功能、手指的运动约束进行了调查,并研究了具有代表性的食指生物力学模型。在此基础上研究了手功能康复机器人的机械结构设计。根据临床康复的需求,考虑穿戴的舒适性,安全性及运动的柔顺性,设计了针对手指多关节运动功能康复的机构。基本的结构采用连杆的外骨骼形式,驱动器选用柔顺性好的气动肌肉。搭建手功能康复机器人控制系统实验平台,并进行了基本的实验研究,验证了机构和控制系统的有效性。同时,还提出了机构改进的方案,为进一步设计真正临床实用化的手功能康复机器人奠定了良好的基础。
针对手功能康复机器人的驱动器——气动肌肉所组成的两种不同形式的驱动关节进行了分析和比较,说明气动肌肉扭力弹簧驱动关节是一种有效的驱动形式,非常适合于手功能康复机器人的应用。由于气动肌肉具有很强的时变和非线性特性,难以精确控制,因此,本文对气动肌肉的建模与控制进行了深入的研究。首先通过实验对气动肌肉的“三元素”模型进行了辨识,并在此基础上设计了滑模控制器,考虑到未建模的动力学以及系统存在的不确定的扰动,采用干扰观测器对这些不确定的非线性扰动进行观测,设计了基于非线性扰动观测器的滑模控制器,仿真和实验结果表明当系统存在不确定的建模误差和外部扰动的情况下,设计的控制方法可以保证系统跟踪性能要求。不同的制作工艺和材料特性会导致气动肌肉具有不同的动态特性,为了寻求更为通用和便捷的建模和控制方法,还采用一种新型的递归神经网络——回声神经网络对气动肌肉进行了建模,并基于回声神经网络设计了自适应控制器,通过仿真和实验进行了比较,说明了方法的有效性。
表面肌电信号反馈也是手功能康复机器人的一个重要组成部分,可用于康复评价和肌电控制策略中的意图检测,因此,本文也对人手表面肌电信号的辨识进行了较为深入的研究。采集人手做屈肘、伸肘、腕内旋、腕外旋、展拳及握拳六种不同动作时的表面肌电信号,利用离散小波分解提取特征值。针对特征值,采用支持向量机进行了分类,取得了很好的分类效果。通过与神经网络分类器的进一步比较可以发现,支持向量机对小规模样本的分类更加有效,具有同神经网络类似的优点,自动提取分类信息和自动特征选择,且具有更好的泛化能力,容易实施和控制,不会产生神经网络的局部极小和过学习的问题。
最后,对全文进行了总结,并指出了下一步需要进行的工作。
本博士论文通过深入研究手功能康复机器人系统的若干关键技术问题,为发展面向临床应用的手功能康复机器人系统提供必要的理论依据、实验数据和研究经验。随着研究工作的进一步深入和完善,手功能康复机器人将最终转化为康复产品,为更多的患者提供康复训练,提高他们的康复效果和生活质量,具有积极的学术和实际意义。
With the increasing number of the hemiplegia patients, attention has been drawn to the rehabilitation therapy of the movement functional disorder. Rehabilitation of the movement function has influenced on patient's daily life greatly. In nowadays, this topic is one of the most difficult problems in rehabilitation therapy and has become a hotspot of modern rehabilitation and medical studies. Rehabilitation robot research was born as a new study area due to the great demand in rehabilitation of the movement function. It involves robotics, rehabilitation medicine, ergonomics, mechanical design, control theory, computer science and other fields.
Under the support grants from National 863 project and National Natural Science Foundation, the research work in this dissertation focus on investigation some key technologies of wearable robot for rehabilitation of hand function. Our study includes mechanical and control system design of rehabilitation robot, modeling and control of pneumatic muscle actuator, identification of hand's surface electromyography (sEMG) signal, and so on. The main contributions of this dissertation focus on the following aspects:
First, the existing rehabilitation equipments for hand function are analyzed and compared. The structure, function and motion constraints of human hand are analyzed and biomechanical model of representative index finger is also investigated. Based on the investigations, the mechanical design of the hand rehabilitation robot is studied. Considering the needs of clinical rehabilitation, the wearing comfort, as well as safety and flexibility of motion, we design a mechanism which can assist fingers to complete multi-joints functional rehabilitation movements. The exoskeleton structure is adopted and the flexible PMs are selected as its actuator. A control system platform of the hand rehabilitation robot is built and the basic control experiments are also implemented to verify the effectiveness of the mechanical and control system. Furthermore, the improved mechnical design is also proposed, which lays a good foundation for realizing the clinical use of the hand rehabilitation robot.
Two different structural actuators consisting of the pneumatic muscle (PM) which is work as rehabilitation robot's actuator are analyzed and compared. The work indicates the proposed actuator configuration consisting of the PM and the torsion spring is an effective and promising method for rehabilitation robot application. The complex nonlinear dynamics and time-varying parameters of the PM make it difficult to control. Thus, the modeling and control of PM are also investigated in this dissertation. We identify three-element model of the PM by experiments. Based on this model, the sliding mode controller is designed. Considering the unmodeled dynamics and uncertain disturbance in the system, the disturbance observer is adopted to compensate for these uncertain nonlinear disturbances. The simulation and experimental results demonstrated that the model and the controller achieved the desired performance in tracking a desired trajectory within guaranteed accuracy regardless of modeling uncertainties and perturbation. The PM made by different workmanship and material has different dynamics. To get more convenient modeling and control method, a new type of recurrent neural networks-Echo Neural Network (ESN) is proposed to model the PMs. The adaptive controller is developed based on ESN. The effectiveness of the proposed method is validated by simulation and experiments.
The other important part of the rehabilitation robot is the sEMG signal feedback, which can be used to evaluate the effectiveness of the rehabilitation and recognize the motion intension. In order to fulfill the sEMG signal recognizing task, sEMG signal of elbow flexion, elbow extension, wrist pronation, wrist supination, palm extension and fist are collected. Then discrete wavelet transform is used to extract the signal's features. The Support Vector Machines (SVM) is used to classify these features. Compared with the neural network classifier, the experimental results show that SVM is more effective for small-scale sample classification. Besides having the advantages of neural network, which can extract the classification information and select features automatically, SVM also has better generalization ability and is easy to apply and control. Furthermore, it overcomes the disadvantages of artificial neural network such as overlearning and partially leading to minimum.
Finally, the summary of this dissertation and the directions of future work are presented.
Some key issues on hand rehabilitation robot system are investigated deeply in this thesis. These works provide the necessary theoretical basis, experimental data and valuable research experience for development of the clinic-oriented hand rehabilitation robot. With improvement of the further research work, some products on hand rehabilitation robot will be realized. These products can provide more patients effective rehabilitation training and improve the quality of rehabilitation. This is a research work of positive academic and practical significance.
引文
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