上肢康复机器人的结构设计【三维UG】【19张PDF图纸+CAD制图+文档】
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原文：RUPERT: An Exoskeleton Robot for Assisting Rehabilitation of Arm FunctionsSivakumar Balasubramanian, Student Member, IEEE, Ruihua Wei, Mike Perez, Ben Shepard, Edward Koeneman, James Koeneman, and Jiping He, Senior Member, IEEEAbstract: The design of a wearable upper extremity therapy robot RUPERT IV (Robotic Upper Extremity Repetitive Trainer) device is presented. It is designed to assist in repetitive therapy tasks related to activities of daily living which has been advocated for being more effective for functional recovery. RUPERT has five actuated degrees of freedom driven by compliant and safe pneumatic muscle actuators (PMA) assisting shoulder elevation, humeral external rotation, elbow extension, forearm supination and wrist/hand extension. The device is designed to extend the arm and move in a 3D space with no gravity compensation, which is a natural setting for practicing day-to-day activities. Because the device is wearable and lightweight, the device is very portable; it can be worn standing or sitting for performing therapy tasks that better mimic activities of daily living. A closed-loop controller combining a PID-based feedback controller and a Iterative learning controller (ILC)-based feedforward controller is proposed for RUPERT for passive repetitive task training. This type of control aids in overcoming the highly nonlinear nature of the plant under control, and also helps in adapting easily to different subjects for performing different tasks. The system was tested on two able-bodied subjects to evaluate its performance. I. INTRODUCTION RECENT advances in neuroscience indicate that the central nervous system can reorganize after injury 1. This increased awareness of neuroplasticity coupled with emerging interests in controlling and measuring the results of therapy has had a profound influence on the rehabilitation engineering community. This influence has generated a lot of interest in upper and lower extremity robotic therapy research. To some extent, the emerging interest in therapy robots was also facilitated by the fact that many of the traditional therapeutic approaches were time consuming. The increased therapist required time for stroke therapy coupled with the projected increase in the number of stroke survivors because of the aging “Baby Boomer” population leads to projections for increased demand for physical therapy time. Robots can reduce the physical effort of therapists and provide them with the ability to concentrate more on therapy performance. Most stroke survivors suffer various degrees of loss in both cognitive and motor functions. Motor control research has shown that sensory motor integration is a key process in motor learning and the subsequent improvement in function. Integrating multi-modal biofeedback with therapy will motivate the stroke survivors to actively and volitionally process sensory information during repetitive therapy for motor function. When the therapy is based on a meaningful purposeful task, cognition is challenged, further enhancing learning. The challenge for therapy robots is to achieve the desired effects of motivation, cognitive challenge, easily customizable tasks specific to therapeutic outcomes, and real-time performance assessment 2-4. A prerequisite for the elaborate movements of the upper extremity, and dexterous abilities of humans in general is the ability to coordinate multiple joints and regulate forces produced by limb segments. While the disturbance of voluntary upper extremity movement in subjects with stroke is typically apparent upon visual examination, little is known about the mechanisms responsible for these disturbances 5. This is due in part to the dearth of quantitative studies of multi-joint movements in such subjects 3. During a simple target-directed pointing task, subjects with stroke could reach into all parts of the workspace with their affected limb, 6 suggesting that movement planning was intact for these subjects. However, when inter-joint coordination was assessed by expressing elbow angle as a function of shoulder angle, subjects with stroke exhibited an irregular and variable relationship. This disruption in inter-joint coordination resulted in movement paths that were more segmented and variable. Roby-Brami and colleagues showed that prehension (reaching and grasping) movements of subjects with stroke were characterized by a spatiotemporal dyscoordination between the arm and trunk 7. As a result subjects developed a new pattern of coordination represented by more trunk recruitment during prehensile actions. More recently, a kinematic analysis of reaching movements of subjects with stroke indicated that subjects with stroke, unlike healthy controls, recruited the trunk to assist in transporting the hand to the object 8. Thus, these subjects were recruiting a new degree of freedom (e.g. the trunk) to perform this task. Kinematic studies have also shown that subjects with stroke have a more variable reaching path, orientation of the hand relative to the object, final hand position on the object, and a disruption in inter-joint coordination. These data suggest that subjects with stroke have difficulty with motor execution. Therefore rehabilitation of the affected upper extremity should be oriented toward restoring the normal sensorimotor relationships between the joints 6. II. METHODS A. Design Basis Motor control research has shown that both subcortical and cortical networks participate in the control and modulation of movement. The principles of neuroplasticity propose that these networks can be “rewired” through repetitive training. It is our hypothesis that in the process of training neurologically injured subjects, therapy tasks should be based on tasks related to activities of daily living in a natural environment. This will encourage the retraining of the subtle coordination in cortical and subcortical networks to perform real-life activities in a natural environment, rather than training in a gravity compensated or any other unnatural environments. Therefore, our device and therapy protocol consists of a wearable device so that skills needed for numerous activities of daily living can be trained in a natural gravity environment. Traditional robots are usually driven by electric motors attached to gear boxes which can be very stiff and can supply very large torques which could result in injury to a stroke survivor with spasticity. Electric motor actuation in wearable robotic applications presents a mismatch in the compliance of the actuator and the limb being assisted. A compliant interface for human interaction requires a system that can easily control resisting forces. The traditional approach to designing a compliant interface for human interaction is to actively control the compliance of motors using appropriate control algorithms such as those developed by Salisbury 9 or Paul 10. The main disadvantage in this method is that in such systems, the mechanical stiffness is typically very large and it is necessary to rely on high-performance actuators (expensive motors) and high bandwidth control systems to provide compliance using robots that are particularly designed for position control tasks. Such schemes have inherent limitations during interaction with stiff environments (i.e., such as repeated impacts with a walking surface). However, a better approach is to build into the system some mechanical compliance and then use active control to vary this compliance. The main advantage of this approach is that there is always some compliance in the system regardless of the stiffness of the environment. As a result, the requirements on the actuator and the control bandwidth are more modest. Therefore, the pneumatic McKibben artificial muscle was chosen as the actuator for our therapy robot. The RUPERT therapy protocols that are being developed to provide the ability to assist with therapies in the same manner that therapists approach stroke patient treatment, i.e., RUPERT should provide proximal stabilization with the capability of providing therapy on isolated joints. After working on the component parts (individual joint motion), therapy progresses to combined motions. Combined motion tasks are developed so the temporal-spatial difficulty of the task can be increased as the motor ability of the subject improves. The tasks are tied to the perception of functional importance to the patient. Eventually RUPERT will have a catalog of tasks from which the therapist/patient can select the therapy tasks of interest. Three tasks are initially being tested: reaching, washing the contralateral arm and drinking. The difficulty of these tasks depends on the target location and desired timing. B. Structural Design If restricting continuous tone exists in stroke survivors, it usually occurs in the “anti-gravity” muscles, i.e., the flexors of the fingers, hand, wrist, and elbow, the pronators of the forearm, the extensors of the shoulder, and internal rotator muscles of the humerus. The design of an earlier version (RUPERT III) has been described 11. The current design (RUPERT IV) has added shoulder external rotation to expand the space available for performing assisted tasks. Assistance and measurement of the following joint motions is provided in RUPERT IV; hand/wrist extension, forearm supination, elbow extension, humeral external rotation and shoulder elevation. To accommodate a large range of subject sizes, adjustments in the structure are provided (three degrees of adjustment at the shoulder, and humeral length, forearm length, hand length adjustment). To keep the weight of the structure low, many of the structural components are made of graphite composite materials. A picture of RUPERT IV is shown in Figure 1. Fig.1.PictureofRUPERTIVC. Control Design The immediate design goal for the RUPERT aims to help the stroke survivors to practice basic arm movement relevant to activities of daily living repeatedly to achieve therapeutic benefit for improved functionality and independence. The challenge for the control system is to provide a consistent performance for stroke survivors who all have different functional impairments and neurological conditions. Safety and comfort impose significant constraint on the design of control algorithm. The hardware of control system for the robot consists two major components, (i) a control box housing a PC104 single board computer, and five pressure regulators that actuate each of the DOF in RUPERT IV, (ii) a main PC (mPC) which acts the terminal for the physician/therapist/operator to interact with the robot. The overall structure of the RUPERT control system has two layers, namely, the Inner Loop (IL) and the Outer Loop (OL) as shown in Figure 2. The IL controller works at individual joint level, while the OL controller works at the level of functional tasks. The current closed-loop controller for RUPERT IV is developed for a passive therapy mode. The controller was designed in a Matlab/Simulink environment on the mPC and loaded into the control box. Fig.2.OverallClosedLoopControlStructure.1. Outer Loop Controller: The OL controller is currently a simple (open loop) trajectory planning module that generates a command trajectory in the joint space for the controllers in the IL. It should be noted that the current version of the OL controller does not form a closed-loop as shown in Fig. 2. Given an initial and target position in joint space, the OL generates a smooth trajectory in the joint space using the following equations, Where, is the command angle, is the initial angle, is the target angle, T is the task duration, and t is time. The above equation is the minimum jerk equation in the Cartesian space 12, but we use it in the joint space in our reaching tasks just to provide a smooth command signal to the IL controllers.2. Inner Loop Controller: Each of the five joint controllers in the IL consists of a PID feedback controller. Additionally, three of the joint controllers (Shoulder flexion/extension, Elbow /flexion/extension, and Humoral rotation) have an Iterative Learning Controller (ILC) in parallel to the PID feedback controller (Fig. 3). Because of the highly nonlinear nature of the plant (robot + Stroke subjects arm) being controlled, the use of a traditional linear PID controller can produce highly varying responses for different command signals and for different subjects. Apart from this, the response becomes slightly oscillatory (or feels jerky) for slower movements; thus, even for a smooth command signal the actual movement appears as if it is composed of many sub-movements. This jerky movement is prominent especially in the joints requiring large PMA (Internal tube diameter in1) to provide a strong force to assist movement. A large PMA has a slower and nonlinear dynamic response, which might be a major reason for the jerky movement. One way to compensate for the slow nature of the actuator is to use a feedforward controller. Since RUPERT will be used for performing different types of tasks at different speeds, it is unlikely that a single feedforward controller will perform equally well for all of these situations. Designing different feedforward controllers for different tasks on different subjects will be an impossible task and make the IL controller too complicated. Fig. 3. Individual Joint Controller with PID and ILC Controller (Shoulder Flexion/Extension, Elbow Flexion/Extension and Humoral Rotation have PID+ILC controller; Elbow Supination/Pronation and Wrist Flexion/Extension have only PID controllers).The repetitive nature of the therapy and training provides a good opportunity for machine learning techniques to generate a logical control approach: the Iterative Learning Control (ILC). The basic idea is that the controller learns from errors measured from the previous trials and updates the control command to optimize the performance. ILC is a type of learned open loop control strategy that is used in applications with repetitive tasks. ILC improves the performance of the system by learning from the previous executions 13. Thus, instead of designing a large rule base with control rules that can provide satisfactory performance for difficult tasks on different subjects, we take advantage of repetitive nature of the therapy. The controller can learn for each individual on the first few trials of each session to update and shape a satisfactory feedforward control command for a given training task. The PID gains for PID+ILC controllers are set to fairly low values to ensure an overdamped response from the controller when there is zero output from the ILC. The low values for the PID gains were chosen primarily to prevent a overshoot in the closed-loop response. It should be noted that overshoots cannot be actively corrected by RUPERT due to the unidirectional nature of the actuation in each DOF. The ILC helps the IL adapt to different subjects by learning from the previous performance of the controller. The learning procedure for the ILC consisting of two separate steps, Step1: Learning from error signal from the previous iteration. Where, is the control signal for the current iteration (j) learned from the previous iterations control signal (uj1), L (q) is the filter applied on the previous iterations error signal (ej1), is the learning rate for the ILC, q is the forward shift operator, and n is the sample number. The value of determines the extent to which an error signal from the current iteration is learned for the next iteration. The learning rate was made a nonlinear function of three performance metrics, to ensure that we learn from the previous performance only when it is necessary. The performance metrics used were, (i) Absolute Mean Error for the previous iteration, (ii) Standard deviation of the absolute mean error for the previous iteration, and (iii) Correlation coefficient of the last two performance error signals. The nonlinear mapping between the learning rate and the three performance metrics is obtained through a fuzzy rule base. The rules for the fuzzy rule base were selected to ensure that the ILC learned only underlying nonlinearities of the plant and not the unwanted disturbances. There were a total of 13 rules used in the rule base. Some of the decisive rules and the rationale behind these rules are as follows, 1. If Absolute Mean Error is VERY LOW then =ZERORationale: Do not learn when the performance is good.2. If Absolute Mean Error is MEDIUM (or HIGH) and Error Correlation is LOW (MEDIUM or HIGH) then = LOW (MEDIUM or HIGH)Rationale: Learn to correct only the errors that are consistent. This is to prevent the controller from learning unwanted disturbances in the system. 3. If Absolute Mean Error is LOW and Variance is LOW (MEDIUM or HIGH) and Error Correlation is HIGH then= LOW (MEDIUM or HIGH)Rationale: Learn any consistent disturbance that causes an increase in the variance of the error signal. Step2: The next step in the ILC learning procedure is to smooth. A smooth ILC control signal is very essential for producing a smooth movement. A smooth is achieved by representing as a high order polynomial function of time. The coefficients of this polynomial function are obtained by performing a least square fit between and. Where, bk s are the coefficients of the polynomial, T is the sampling time, NT is the total number of samples from an iteration, and N is the order of the polynomial. The order of the polynomial was chosen as 20 for our learning algorithm. The final control signal (uj) from the IL is a weighted combination of the PID feedback control signal () and the ILC ()Where, WFB is the weight of the PID controller and WILC is the weight on the ILC. III. RESULTS The designed closed-loop controller was tested on two able-bodied performing reaching tasks. The main objective of the testing procedure was to see if the controller was able to adapt to different subjects for different reaching tasks. During the reaching tasks, the subjects (especially the able-bodied subjects) were instructed to remain passive at the shoulder, and simulate some tone at the remaining degrees-of-freedom. This is necessary to prevent overshoots in the controller response, which cannot be actively corrected by the controller. Figure 4 shows the response of the closed-loop controller for the first four reaching tasks involving the shoulder, elbow flexion/extension and humoral rotation. The command signals for these three degrees-of-freedom and the corresponding response of the closed-loop controller are shown in Figure 4 for four consecutive reaching trials. The increased convergence between the command angle (blue trace) and the actual angle (red trace) can be seen with each trial. The convergence between the command and actual angle was quantified using the absolute mean error between these two signals. The absolute mean error for the four trials shown in Fig 4 is plotted in Fig 5 as a function of the trial number. This figure clearly demonstrates the improvement in the performance of the PID+ILC controller. IV. DISCUSSION & CONCLUSIONSThe design of a five degree-of-freedom exoskeleton wearable robot, along with a closed-loop controller has been presented. An adaptive controller combining a PID-based feedback controller and a ILC-based learning controller is proposed for RUPERT. The ability of the PID+ILC controller to adapt to different subject and different tasks was tested on two able-bodied subjects and the performance of the controller indicated that controller was able to adapt to the two different subjects. Our current research is focused towards improving the functionality of RUPERT. The following are proposed as the future work: 1. Implementation of proposed controller for tasks other than simple reaching tasks (multiple point-to-point reaching in a 3-D workspace and arm washing tasks).2. Develop a control scheme for implementing an active-assist therapy mode with RUPERT. There are indications that where a patient is motivated and premeditates their movement, the recovery is more effective. So the user-driven movement trajectories, patient-cooperative mode and minimal intervention principle should be implemented. The OL controller will be the heart of the active-assist therapy mode. It will monitor the subjects movement and makes decisions on “when” and “how” to provide assistance based on the kinematics of the subjects movement. Fig.4.ResponseofthePID+ILCcontrollerforanablebodiedsubjectperformingfourconsecutivereachingtaskswithRUPERTFig.5.Absolutemeanerrorvs.trialnumber译文：鲁珀特:协助手臂功能康复的外骨骼机器人摘要：可穿戴上肢疗法机器人鲁珀特四世 (机器人上肢重复教练)设备的设计出现了。它设计的目的是协助日常生活中相关活动的重复治疗任务，一直主张对功能恢复具有更好的效果。鲁珀特有五个动作自由度，被兼容和安全气压肌肉致动器(PMA)所驱动，协助肩膀高度，肱骨的外部旋转，手肘扩展，前臂旋后和手腕/扩展。该设备是为了延长手臂和3d空间内没有重力补偿的情况下进行移动而设计，为患者提供练习日常活动的自然环境。因为设备是可穿戴而且很轻,所以设备很轻便，穿上它可以站立或坐着执行治疗任务，更好地模仿日常生活活动。鲁珀特提出闭环控制器结合基于PID反馈控制器和基于迭代学习控制(ILC)的前馈控制器的被动重复性任务训练。这种控制类型能够帮助克服高度非线性的植物自然，同时也有助于容易适应不同主题来执行不同的任务。系统通过测试两个强劲的主题来评价其性能。I. 绪论 最近的神经科学的发展表明,中枢神经系统损伤后可以重组1。这种先进的神经可塑性的意识再加上在控制和测量的治疗结果上的新兴利益对康复工程方面产生了深远的影响。这种影响滋生了大量的关于上、下肢机器人治疗研究的利益。在某种程度上，许多传统的治疗方法是费时，这样一个事实也促成了治疗机器人新兴利润的产生。治疗师治疗中风所需时间增加再加上预计中风幸存者的数量的增多，由于老龄化“婴儿潮”人口数量会导致物理治疗时间需求增加的预测。机器人可以减少治疗师身体上的努力，也为他们提供更加专注于治疗性能的能力。 大多数中风幸存者在认知和运动功能上都遭受了不同程度的损失。运动控制的研究表明,感觉运动集成在运动学习和后续的功能改进方面是一个关键的过程。在重复治疗运动机能过程中，集成多模式生物反馈疗法将激励中风幸存者积极地和有意志地处理感觉信息。当治疗是基于有意义有目的的任务,认知是进一步加强学习的挑战。治疗机器人面临的挑战是实现动机，认知的挑战，针对特定的治疗结果轻松地定制任务和实时性能评估的期望效果2 - 4。 上肢的复杂的运动的一个先决条件和人类一般情况下灵巧的能力是能够协调多个关节和肢体部分产生的监管力量。虽然患有中风的志愿者上肢运动的干扰通常在视觉检查上是非常明显的，但这些干扰负责的机制尚不清楚5。一部分是由于在这样的主题中缺乏关节运动的定量研究3。在一个简单的定向任务，中风的问题可能进入所有受影响的肢体的工作空间，6表明了运动计划对这些主题是完好无损。然而，当协调被表达肘角作为肩角的函数所评估时，中风的主题显示出一种不规则和变量之间的关系。这一中断导致运动路径具有更多分段和变量。罗比-布拉米和他的同事们发现，中风(达到和把握)的特点是手臂和躯干间的时空不协调7。由于受试者的协作开发了一种新模式，这种模式在抓握的动作中被取代。最近，中风患者运动学分析的主题表明受试者不同于健康对照组，他们招募了一些人协助传递物品8。因此，这些受试者招募一个新的自由度(如主干)来执行这项任务。运动的研究也表明，患有中风的受试者具有更多方式达到途径，一方面相对于物品的定位,最终手处于物品的位置，最终协调中断。这些数据表明,患有中风的受试者打开电机有困难。因此影响上肢的康复应注重于恢复运动关节之间正常感觉的关系6。II. 方法 A. 设计基础电机控制的研究表明，大脑皮层和皮层下网络参与运动的控制和调制。神经可塑性的原则提出，这些网络可以通过重复训练来进行“重塑”。我们假设训练神经受伤的过程中，治疗任务应该基于自然环境下日常生活活动相关的任务。这将鼓励自然环境下现实活动中皮层和皮层下网络的协调执行训练，而不是训练在重力补偿或者在其他非自然的环境中。因此,我们的设备和治疗协议包含一个可穿戴设备，以至于众多日常生活活动所必需的技能可以在自然重力环境下被训练。传统的机器人通常由电动马达与齿轮箱连接进行驱动，它非常僵硬同时提供非常大的扭矩导致中风幸存者处于痉挛状态。可穿戴机器人应用程序的电动马达驱动体现了执行机构和辅助肢体上的不匹配。一个人机交互的兼容界面需要一个可以很容易地控制抵抗力的系统。设计一个兼容的人机交互界面的传统方法是通过积极控制汽车使用适当的控制算法的合规化，如由索尔兹伯里9或保罗10研发的机器人。这种方法的主要缺点是，在这样的系统中,机械刚度通常是非常大的，并且需要依靠高性能驱动器(昂贵的马达)和高宽带控制系统去提供使用专门用于位置控制任务而设计的机器人的合规化。在僵硬的环境中交流，这样的计划中具有特定的局限性(即交互，如表面行走的重复影响)。然而，更好的方法是构建成一些机械系统合规，然后使用主动控制来改变这种合规。这种方法的主要优势就是不管环境的刚度如何总有一些合规系统。最终，关于执行机构和控制宽带的要求就会更加适度。因此，气动人造肌肉被选定为我们治疗机器人的执行机构。鲁珀特治疗协议，正在开发提供能够以与治疗中风病人的治疗方法相同的方式协助治疗的能力，即鲁珀特应该提供孤立关节治疗能力的近端稳定。研究完组成部分(个人联合运动)之后,治疗发展到运动相结合。因为组合运动相对发达，运动能力有所提高，所以时空任务的难度将会增加。任务与病人对功能重要性的感知相联系。鲁珀特最终会有一个任务的目录，在这个目录当中治疗师/病人可以选择感兴趣的治疗任务。三个任务正在进行初步测试：达到洗侧手臂和喝酒。这些任务的难度取决于目标位置和所需的时间。B. 结构设计如果限制风幸存者连续色调的存在中，它通常发生于“反重力”的肌肉当中，比如手指、手、手腕、手肘、前臂的和肩膀上的伸展肌，内部肱骨的回旋肌的屈肌。早期版本的设计(鲁珀特三世)已经被描述过了11。当前设计(RUPERT IV)为执行辅助任务增加了肩外部旋转以扩大可用空间。鲁珀特四世提供了下列联合动作的援助和测量；手/手腕伸展，前臂旋后，手肘扩展，肱骨的外部旋转和肩膀的抬高。为适应大范围的患者尺寸，提供了结构上的相关调整(肩膀的3度调整，肩的长度、前臂长度、手长度的调整)。为了保持结构重量的轻盈，许多结构组件是由石墨复合材料所造。如图1所示为鲁珀特四世的照片。图1为鲁珀特四世的照片C. 控制设计鲁珀特的直接设计目的旨在帮助中风幸存者实践相关的日常生活活动中的基本手臂运动，有利于改善功能和独立而达到治疗的效果。控制系统面临的挑战是为有着不同的功能障碍和神经系统问题的中风幸存者提供一种一致的性能。安全和舒适在控制算法的设计上施加显著的约束。机器人控制系统的硬件由两个主要部件组成，(i)一个装着PC104单板计算机的控制箱和五个促使鲁珀特四世每个自由度的压力监管机构，(ii)一个主要PC(mPC)在终端为外科医生/医生/运营商与机器人进行互动。如图2所示，鲁珀特控制系统的总体结构有两个层次，即内循环(IL)和外循环(OL)。IL控制器在个人联合工作水平下工作，而OL控制器在功能任务水平上工作。鲁珀特四世的电流闭环控制器被一个被动的治疗模式所开发。控制器是在Matlab / Simulink环境中在mPC设备上设计出来的并且被加载到控制箱上。图2 整体闭环控制结构外回路控制器:OL控制器目前是一个简单的
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