基于模糊逻辑的上肢康复机器人阻抗控制实验研究

针对机器人辅助患肢主动康复训练过程中辅助力／阻力不能随患肢病情实时调整的问题,提出了一种 新的模糊自适应阻抗力控制方法．该方法实时检测患肢与机器人之间的相互作用力,并进一步运用辨识算法实时估 计出患肢的病情状态;然后运用模糊阻抗控制器对两者之间的相互作用力进行实时调整,使得在患肢主动能力不 足时提供一定的辅助,而在其有能力完成动作时,实时调整阻力实现肌力训练．实验结果表明了该控制方法的有效 性．     

基于阻抗模型的下肢康复机器人交互控制系统设计

为帮助下肢功能障碍患者进行康复训练,设计了下肢康复机器人。对于该机器人的控制,采用传统系统无法柔顺控制,导致机器人运动轨迹偏离预设轨迹。针对该现象,提出了基于阻抗模型的下肢康复机器人交互控制系统设计。通过分析总体控制方案,设计系统硬件结构框图。采用L型二维力传感器,确定两个方向的人机交互力。使用绝对值编码器安装在各个关节处,其输出值作为髋关节、膝关节、踝关节电机的转动位置,增量编码器安装在电机轴上,测量值用来作为后期控制方法的输入参数。构建阻抗控制模型,能够调节机器人位置和速度,具有消除力误差功能。依据此力矩对参考运动轨迹进行设计,实时获取患者康复训练的跟踪、主动柔顺和接近状态信息。在柔顺训练实验测出人机交互力,通过实验结果知,在检测到人体主动力矩异常时,系统能够重新优化轨迹,具有良好柔顺控制效果。     

Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Control system implementation is one of the major difficulties in rehabilitation robot design. A newly developed adaptive impedance controller based on evolutionary dynamic fuzzy neural network (EDRFNN) is presented, where the desired impedance between robot and impaired limb can be regulated in real time according to the impaired limb??s physical recovery condition. Firstly, the impaired limb??s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using a slide average least squares (SALS)identification algorithm. Then, hybrid learning algorithms for EDRFNN impedance controller are proposed, which comprise genetic algorithm (GA), hybrid evolutionary programming (HEP) and dynamic back-propagation (BP) learning algorithm. GA and HEP are used to off-line optimize DRFNN parameters so as to get suboptimal impedance control parameters. Dynamic BP learning algorithm is further online fine-tuned based on the error gradient descent method. Moreover, the convergence of a closed loop system is proven using the discrete-type Lyapunov function to guarantee the global convergence of tracking error. Finally, simulation results show that the proposed controller provides good dynamic control performance and robustness with regard to the change of the impaired limb??s physical condition.     

机器人辅助上肢关节运动控制与临床实验研究

针对机器人辅助患肢被动康复训练过程中关节活动度(ROM)及运动控制参数不能随患肢病情实时调整的问题,提出一种新的模糊自适应关节被动运动闭环监督控制方法.该方法首先根据患肢关节活动恢复程度设计上层监督控制器,得到符合患肢病情的关节期望运动范围;再通过设计下层闭环位置跟踪控制器,控制机器人平稳地牵引患肢关节沿目标轨迹进行训练.临床实验结果验证了所提算法的有效性.     

基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

设计了一种基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法.在机器人与人体上肢接触面安装力传感器采集人机交互力矩信息作为量化的主动运动意图,设计了一种无模型自适应滤波算法使交互力矩变得平滑而连贯;以人机交互力矩为输入,综合考虑机器人末端点与参考轨迹的相对位置和补偿力的信息,设计了人机交互阻抗控制器,用于调节各关节的给定目标速度;设计了将无模型自适应与离散滑模趋近律相结合的速度控制器完成机器人各关节对目标速度的跟踪.仿真结果表明,该控制方法可以实现外骨骼式上肢康复机器人辅助患者完成主动交互训练的功能.通过调节人机交互阻抗控制器的相应参数,机器人可以按照患者的运动意图完成不同的主动交互训练任务,并在运动出现偏差时予以矫正.控制器在设计实现过程中不要求复杂准确的动力学建模和参数识别,并有一定的抗干扰性和通用性.     

Voltage-based adaptive impedance force control for a lower-limb rehabilitation robot

This paper presents a novel voltage-based adaptive impedance force control for a lower limb rehabilitation robot. The impedance parameters are adaptively regulated by a gradient descent algorithm for adjusting the human force in performing therapeutic exercises. Although the proposed control is based on voltage control strategy, it differs from the common torque control strategy. One of the advantages is that it is free from the dynamical models of the robot and patient. Compared with a torque control scheme, it is simpler, less computational, and more efficient while it considers the actuators. The control approach is verified by stability analysis. Simulation results show the efficiency of the control approach applied on a lower limb rehabilitation robot driven by an electric motor. A comparison on performing isometric exercise shows that the voltage-based adaptive impedance force control is superior to both voltage-based impedance control and torque-based impedance control.     

实时上肢参数辨识的康复机器人力控制研究

针对病人进行康复训练时,上肢动力学参数估计不准确和训练过程发生上肢动力学参数变化,所导致康复机器人系统辅助力计算不准确,影响精确和稳定的控制练训。为减小辅助力计算误差,实现精确和稳定的训练控制,基于阻抗控制算法,使用多元线性回归方法对上肢动力学参数进行辨识,提出了一种实时上肢动力学参数辨识的阻抗控制算法,建立了康复机器人动力学模型,同时对控制算法进行仿真研究。仿真结果表明该算法能够准确地对上肢动力学参数进行辨识,有效地消除了辅助力计算误差,实现训练过程中训练轨迹精确控制。     

上肢康复机器人实时安全控制

针对上肢辅助康复机器人临床使用中的安全性和平稳性问题,提出基于模糊逻辑的实时在线安全监测控制方法.机器人对患肢进行康复训练时,患肢状态对控制效果会产生影响;通过设计智能安全监控模糊控制器(SSFC)改善系统运动平稳性以及突发情况下的安全性.首先提取相关运动特征评估受训患肢状态稳定情况,安全监控模糊控制器智能实现正常扰动情况下的控制期望力调节以及突发情况下的紧急响应.其次通过基于位置的阻抗控制策略实现患肢与机器人末端的柔顺性.实验结果验证了该控制方法能够有效地实现康复机器人的安全性和平稳性.     

基于力阻抗模型的上肢康复机器人交互控制系统设计

传统上肢康复机器人交互控制系统受到奇异位形影响,导致系统控制精准度较低,为此提出基于力阻抗模型的上肢康复机器人交互控制系统;设计上肢康复机器人交互控制系统结构,选取双串口12CSA60S2系列单片机作为下位机控制核心模块,利用椎齿轮改变驱动力方向,设计机械臂肘部结构,通过同步带传动,将器件隐藏于空手柄中;设计机械臂腕部结构,满足临床康复时上肢患者站姿与坐姿训练需求;选择箔式应变片BF350力传感器,设计电阻应变片桥接电路,处理传输信号;构建机器人目标阻抗模型,设计基于力阻抗控制策略,调节位置、速度和关节;为改善奇异位形情况,在奇异位形附近关节角速度指令直接由各个关节力矩阻尼控制得到,实现角速度精准输出,完成系统控制;由实验结果可知,该系统直线运动位置、旋转关节位置和伸缩关节位置跟踪结果与标准值基本一致,满足系统设计需求。     

Pneumatic impedance control of a 3-d.o.f. physiotherapy robot

Stroke is a common condition resulting in 30 000 people per annum left with significant disability. In patients with severe arm paresis after stroke, functional recovery in the affected arm is poor. Inadequate intensity of treatment is cited as one factor accounting for the lack arm recovery found in some studies. Given that physical therapy resource is limited, strategies to enhance the physiotherapists' efforts are needed. One approach is to use robotic techniques to augment movement therapy. A 3-d.o.f. pneumatic robot has been developed to apply physiotherapy to the upper limb. The robot has been designed with a workspace encompassing the reach-retrieve range of the average male. Slight non-linearities in the response of the pneumatic system are observed and explained. Building upon previous work that used an error-prone custom force sensor, a commercial 6-d.o.f. force sensor is used to apply impedance control in 3 d.o.f. on the robot.     

下肢康复机器人的自适应人机交互控制策略

针对康复机器人运动过程中的人机交互性问题,提出一种下肢康复机器人自适应人机交互控制策略.提取伸屈运动中下肢表面肌电信号（Surface electromyography,sEMG）和足底压力特征,分别用于表征下肢运动意图和人机交互力（Interaction force,IF）信息,建立基于sEMG-IF的人机交互信息融合模型,实现下肢康复机器人运动轨迹的在线规划;考虑主动康复运动过程中的人机交互作用,建立具有时变动态特性的人机系统动力学模型,设计间接模糊自适应控制器对期望轨迹进行跟踪控制,实现下肢康复机器人自适应人机交互控制.通过对5名被试者进行下肢康复机器人运动控制实验研究,验证所提方法的可行性和有效性.     

下肢软质康复外骨骼机器人的模糊神经网络阻抗控制

针对脑卒中或交通意外等因素导致的运动功能障碍问题,设计了一种可用于康复训练的可穿戴式的软质膝关节外骨骼机器人．在重点介绍基于Hill肌肉模型的套索人工肌肉驱动系统设计和实时控制平台的基础上,分析了模糊神经网络阻抗控制算法的推导过程．最后,分别在定阻抗与变阻抗参数控制策略条件下,进行人机协同训练模式下的康复训练实验,并对比分析了康复外骨骼系统对受试者肌肉活性的影响．实验结果表明,定频率定幅值训练时的屈/伸扭矩分别增加了9.70%和9.06%,而变频率变幅值训练时的屈/伸扭矩提升了88.34%和57.68%．由此可知,选择符合人体生理肌肉刚度特性的阻抗模型可以改善下肢康复机器人系统的稳定性和安全性,提高人机交互的柔顺性和协调性．     

上肢康复外骨骼机器人的模糊滑模导纳控制

为了辅助上肢运动功能障碍患者进行不同模式的康复训练,基于上肢康复外骨骼机器人系统,提出了一种模糊滑模导纳控制策略,实现训练过程的人机协调控制.首先,介绍了康复外骨骼的整体结构和实时控制平台.然后,分析了模糊滑模导纳控制算法的推导过程,并根据李亚普诺夫稳定性判据证明系统的稳定性.最后,在不同系统导纳参数条件下,分别进行被动训练模式和主动训练模式实验,并对比分析了实验过程中运动偏差、人机交互力以及肱二头肌表面肌电信号的变化特点.实验结果表明,选择合适的目标导纳模型可以优化康复训练强度与难度,提高人机交互柔顺性与患者参与度,满足不同瘫痪程度和康复进度患者的训练需求.     

sEMG-based impedance control for lower-limb rehabilitation robot

Recently, various rehabilitation robots have been developed for therapeutic exercises. Additionally, several control methods have been proposed to control the rehabilitation robots based on user’s motion intention. One of the common control methods used is torque-based impedance control. This paper presents an electromyogram-based robust impedance control for a lower-limb rehabilitation robot using a voltage-based strategy. The proposed control strategy uses surface electromyogram (sEMG) signals in place of force sensors to estimate the exerted force. In addition, the control is based on the voltage control strategy, which differs from the common torque control strategies. For example, unlike the torque-based impedance control, the controller is not dependent on the dynamical models of the patient and the robot. This is particularly important as the dynamic of the patient is both difficult to model precisely and changes during the rehabilitation period. These simplifications results in a significant reduction in calculation time. To illustrate the effectiveness of the control approach, a 1-DOF lower-limb rehabilitation robot is designed. Experimental sEMG-force data are collected and used to train an artificial neural network. Simulation results show that compared with a torque-based control approach, the voltage-based is simpler, less computational and more efficient while it considers the presence of actuators. Finally, we design an adaptive fuzzy system to estimate and compensate the uncertainty in performing the impedance rule. The adaptive fuzzy system has an advantage that does not need new feedback to estimate the uncertainty. The control approach is further verified by stability analysis. Simulation results show the efficiency of the control approach in performing some therapeutic exercises.     

Muscle circumference sensor and model reference-based adaptive impedance control for upper limb assist exoskeleton robot

In this paper, we have addressed two issues for upper limb assist exoskeleton: (1) estimation of human desired motion intention (DMI) using non-biological-based sensors; and (2) compliant control using model reference-based adaptive approach. For non-biological-based DMI estimation, we have employed Muscle Circumference Sensor (MCS) and load cells. MCS measures human elbow joint torque using human arm kinematics, biceps/triceps muscle model, and physiological cross-sectional area of these muscles. So, using MCS, we have measured Biceps/Triceps internal muscle activity and we have tried to reduce it by providing robotic assistance. To extract DMI, we have employed radial basis function neural network (RBFNN). RBFNN uses position, velocity, and human force to estimate DMI which is further tracked by the impedance control law. This algorithm is based on model reference-based adaptive impedance control law which drives the overall assist exoskeleton to the desired reference impedance model, giving required compliance. To highlight the effectiveness, we have compared proposed control algorithm with simple impedance and adaptive impedance control algorithms. Experimental results demonstrate the reduced muscle activity and active compliance for subject wearing the robot.     

A Study on Kinematics and Workspace Determination of a General 6-<Emphasis Type="Underline">P</Emphasis> US Robot

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.     

Active torque-based gait adjustment multi-level control strategy for lower limb patient–exoskeleton coupling system in rehabilitation training

This paper proposes an active torque-based gait adjustment multi-level control strategy for lower limb patient–exoskeleton coupling system (LLPECS) in rehabilitation training. The proposed controller has three levels of high, middle, and low sub-controllers: gait adjustment layer (high-level), interaction torque design layer (middle-level), and trajectory tracking layer (low-level). The high-level sub-controller uses an adaptive central pattern generator (ACPG) to adjust the desired gait for rehabilitation training according to the patient’s active torque. In the middle-level sub-controller, the desired interaction torque is designed with neural networks and the estimated muscle torque by utilizing nonlinear disturbance observer (NDO). In the low-level sub-controller, a time delay estimation-based prescribed performance model free control is designed for the accurate tracking performance of the exoskeleton, so as to make the actual interaction torque track the desired value. An exoskeleton virtual prototype, which is developed in SolidWorks, has been imported to MATLAB/Simulink to conduct co-simulations in the SimMechanics environment. The results of co-simulations demonstrate the effectiveness of the proposed control strategy when the patient’s muscle torque is at different recovery degrees.     

Robo-Erectus: a low-cost autonomous humanoid soccer robot

An exoskeleton robot can replace the wearer's motion function by operating the human's body. The purpose of this study is to propose a power assist method of walking, standing up and going up stairs based on autonomous motion of the exoskeleton robot suit, HAL (Hybrid assistive Limb), and verify the effectiveness of this method by experiment. In order to realize power assist of tasks (walking, standing up and going up stairs) autonomically, we used the Phase Sequence control which generates a task by transiting some simple basic motions called Phases. A task was divided into some Phases on the basis of the task performed by a normal person. The joint moving modes were categorized into active, passive and free modes according to the characteristic of the muscle force conditions. The autonomous motions which HAL generates in each Phase were designed corresponding to one of the categorized modes. The power assist experiments were performed by using the autonomous motion with a focus on the active mode. The experimental results showed that the wearer's muscle activation levels in each Phase were significantly reduced. With this, we confirmed the effectiveness of the proposed assist method.     

康复机器人患肢参数的在线辨识

根据患肢训练时力和位置等信息反馈,提出用解超定方程组的方法在线辨识患肢的动力学参数,实现患肢动力学模型的在线辨识,为远程康复训练机器人系统的实时控制提供较为准确的依据.仿真实验验证,该方法能较好地改善系统的动态性能,使系统具有较好的稳定性和鲁棒性.     

康复机器人的同步主动交互控制与实现

提出了一种适用于康复机器人的人机交互控制方法. 结合一款具有平面并联结构的上肢康复机器人, 实现了与用户(患者)运动意图同步的、柔顺的主动康复训练. 在训练中, 利用自适应频率振荡器, 从表面肌电信号(Surface electromyography, sEMG)中获取运动模式信息, 然后结合运动模式和期望的正常运动轨迹, 生成与主动运动意图同步的参考训练轨迹. 本文通过仿真和实际实验对所提出的方法进行了验证, 振荡器可以在2~5s内快速实现与用户主动运动意图的同步, 然后利用阻抗控制器给予柔顺的辅助. 通过调节阻抗参数, 可以为患者的运动训练提供不同程度的辅助.