上肢康复机器人及相关控制问题研究
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摘要
随着我国迈入老龄化社会，因老年化引起的中风患者发病率逐年增加。患者中风后其运动功能的重建和康复也就引起了研究者的关注。随着康复医学、机械设计以及控制理论等学科的交叉发展和融合，康复机器人相关理论与技术得到了深入的研究和探讨。
在国家863计划和自然科学基金的资助下，本文分别研制了采用电机和气动肌肉不同驱动方式的两款上肢康复机器人。基于所研制的上肢康复机器人，采用肌电信号实现了基于人体运动意图控制的康复训练；此外，在气动肌肉建模的基础上，分别提出了基于模糊补偿的滑模控制算法和基于非线性干扰观测器的动态面控制算法去实现气动肌肉的高精度控制；考虑气动肌肉建模的复杂性和人-上肢康复机器人动力学模型因人而异的差异性，针对模型未知的情况，采用回声状态网（Echo StateNetwork，ESN）提出了基于ESN的PID控制算法和基于粒子群优化及ESN的单层神经网络预测控制算法。主要内容如下：
基于电机驱动研制出三自由度的上肢康复机器人，可实现肩部伸/屈、外展以及肘部伸/屈。在考虑可穿戴性、灵活性、安全性和柔顺性等性能的基础上，又研制出基于气动肌肉驱动的五自由度上肢康复机器人，可实现肩部伸/屈、旋转、肘部伸/屈、指掌关节伸/屈以及指间关节伸/屈。为了减小机械结构和控制系统的复杂性，提出了气动肌肉-扭簧和气动肌肉-拉簧驱动器实现康复机器人关节的双向运动。该款上肢康复机器人不仅可同时进行臂部和手部的康复训练，而且还能分开使用。这大大提高了上肢康复机器人的临床适用范围，为进一步临床实用化奠定了良好的基础。
在采集的四路手臂肌电信号的基础上，选取结点能量作为运动意图特征，实现基于主动运动意图的人-上肢康复机器人交互控制。在采用结点能量作为特征进行特征降维时，提出了一种伴随状态树的方法来消除结点能量的线性相关性以避免类内离散度矩阵奇异问题。降维后的特征向量输入到BP神经网络进行运动意图辨识，取得了很好的辨识效果。同时考虑虚拟现实（Virtual Reality）可使得康复训练充满娱乐性，所以基于辨识的意图动作开发了一款虚拟现实游戏来激发患者在任务关联的训练中交互的主动性和积极性。结合上肢康复机器人、肌电辨识和虚拟现实游戏进行了相关实验研究，结果验证了整个系统的可用性。
作为一种驱动器，气动肌肉因具有很强的时变和非线性特性，这使得其在实际应用中难以实现高精确控制。本文采用“三元素”模型进行了气动肌肉参数辨识和建模，并在此基础上提出了基于模糊补偿的气动肌肉滑模控制。这种控制算法在存在建模误差以及外界不确定扰动时，仍能有效提高控制精度，保证系统跟踪控制性能。此外，还提出了基于非线性干扰观测器的气动肌肉动态面控制。仿真和实际实验结果均验证了所提出算法的可行性和有效性。但基于气动肌肉建模的模型控制，对于长度、形状等不同参数的气动肌肉都需重新进行气动肌肉建模实验，这使得建模过程相对比较复杂。同时，人机动力学模型也因人而异，难以得到精确的、统一的数学模型。基于以上原因，为了解决气动肌肉在康复机器人实际应用中存在的控制精确问题，针对控制算法设计时模型未知的情况下，提出了基于ESN的PID控制算法和基于粒子群优化及ESN的单层神经网络预测控制算法。这些新提出的控制算法均能在模型未知的情况下取得很好的控制效果。
最后，对全文工作进行了总结，并提出了下一步需要改进和完善的工作。
总之，本文通过深入研究上肢康复机器人的设计及相关控制问题，为进一步实现上肢康复机器人在临床上的应用提供了一些设计方法、训练策略、控制算法等研究经验。随着研究工作的深入和完善，期待上肢康复机器人最终能转化为实际应用产品，为更多的患者提供有效的康复训练，帮助他们实现运动功能的改善和重建。
As China is entering the aging society, the stroke incidence is increasing year by year.The reconstruction and rehabilitation of motion function for the patients after stroke alsoattract the researchers’ attention. With the interdisciplinary development and integration ofthe rehabilitation medicine, mechanical design and control theory, the theory andtechnology about rehabilitation robot have been further studied and explored.
Under the support of National863project and National Natural Science Foundation,this paper developed two kinds of upper limb rehabilitation robots, which are driven bymotor and pneumatic muscle (PM), respectively. Based on the designed upper limbrehabilitation robot, with the use of Electromyography (EMG) signal, the human motionintention control based rehabilitation training is realized. In addition, based on PMmodeling, a fuzzy compensation based sliding mode control (FCBSMC) algorithm and anonlinear disturbance observer based dynamic surface control (NDOBDSC) algorithm areproposed for the high-accurate control of PM, respectively. Considering that thecomplexity of PM modeling and the dynamic model of human-upper limb rehabilitationrobot varies from individual to individual, an echo state network (ESN) based PID controlalgorithm and an ESN based single-layer neural network predictive control algorithm withparticle swarm optimization are proposed for the control of unknown model by using ESN.The main contributions focus on the following aspects:
A three degrees of freedom (DOF) upper limb rehabilitation robot driven by motor isdesigned. It can achieve shoulder extension/flexion, shoulder abduction, and elbowextension/flexion. Considering that the performance of wear, flexibility, safety andcompliance, a five DOF upper limb rehabilitation robot driven by PM is developed. It canrealize shoulder extension/flexion and rotation, elbow extension/flexion,metacarpophalangeal and proximal interphalangeal joint extension/flexion. In order toreduce the complexity of the mechanical structure and control system, PM-torsion springand PM-pull spring actuators are proposed to realize the bi-directional movement for thejoints of rehabilitation robot. This robot can realize the rehabilitation training of arm andhand at the same time, as well as be used separately. This greatly extents the availablerange for clinical application, and lays a good foundation for further clinical utility.
By collecting four-channel EMG from arm, the node energy is selected as the featureof motion intention, and the active motion intention based human-upper limbrehabilitation robot interactive control is realized. During the feature dimension reduction,a companion state tree (CST) algorithm is proposed to eliminate the linear correlation of node energy and avoid the singular problem of the within-class scatter matrix. Thereduced feature vector is fed into a BP neural network to recognize the motion intention,and the recognition result is satisfactory. Considering that virtual reality (VR) can makerehabilitation training entertaining, as a result, a VR game based on the motion intentionrecognition is designed to stimulate the patients’ interactive initiative and enthusiasm inthe task-oriented training. The relevant experiments are carried out by the combination ofthe upper limb rehabilitation robot, EMG recognition and virtual reality game to verify theavailability of the whole system.
As an actuator, PM has strong time-varying and nonlinear characteristics, whichmake it difficult to achieve high-accurate control in practical application. Thethree-element model is introduced for PM parameter identification and modeling. On thisbasis, FCBSMC is developed for PM control. This control algorithm can effectivelyimprove the control accuracy and ensure the tracking control performance in the conditionof the existence of modeling error and external uncertain disturbances. In addition,NDOBDSC is also proposed. The simulation and practical experimental results bothdemonstrate the feasibility and effectiveness of the proposed algorithm. However, themodel control based on PM modeling is required to do the PM modeling experiment again,when PM is selected with different parameters, such as length, shape and so on.Meanwhile, the whole dynamic model of human-robot depends on the conditions ofdifferent individual. It is difficult to attain an accurate and uniform mathematical model.For above reasons, to solve the problem on the high-accurate control of PM in thepractical application of rehabilitation robot, under the condition of unknown model, thispaper proposes an echo state network (ESN) based PID control algorithm and an ESNbased single-layer neural network predictive control algorithm with particle swarmoptimization, which can both obtain good control performance.
Finally, the summary of this dissertation and the future work are presented.
To sum up, the design and related control problem on upper limb rehabilitation robotare investigated deeply in this dissertation. These studies provide some researchexperience on the design methods, training strategies and control algorithms for theclinic-oriented application of the upper limb rehabilitation robot. With the furtherimprovement of the research, upper limb rehabilitation robots are expected to transforminto some practical products, which can provide effective rehabilitation training for morepatients, and improve the quality of life and realize the reconstruction of motion function.
在国家863计划和自然科学基金的资助下，本文分别研制了采用电机和气动肌肉不同驱动方式的两款上肢康复机器人。基于所研制的上肢康复机器人，采用肌电信号实现了基于人体运动意图控制的康复训练；此外，在气动肌肉建模的基础上，分别提出了基于模糊补偿的滑模控制算法和基于非线性干扰观测器的动态面控制算法去实现气动肌肉的高精度控制；考虑气动肌肉建模的复杂性和人-上肢康复机器人动力学模型因人而异的差异性，针对模型未知的情况，采用回声状态网（Echo StateNetwork，ESN）提出了基于ESN的PID控制算法和基于粒子群优化及ESN的单层神经网络预测控制算法。主要内容如下：
基于电机驱动研制出三自由度的上肢康复机器人，可实现肩部伸/屈、外展以及肘部伸/屈。在考虑可穿戴性、灵活性、安全性和柔顺性等性能的基础上，又研制出基于气动肌肉驱动的五自由度上肢康复机器人，可实现肩部伸/屈、旋转、肘部伸/屈、指掌关节伸/屈以及指间关节伸/屈。为了减小机械结构和控制系统的复杂性，提出了气动肌肉-扭簧和气动肌肉-拉簧驱动器实现康复机器人关节的双向运动。该款上肢康复机器人不仅可同时进行臂部和手部的康复训练，而且还能分开使用。这大大提高了上肢康复机器人的临床适用范围，为进一步临床实用化奠定了良好的基础。
在采集的四路手臂肌电信号的基础上，选取结点能量作为运动意图特征，实现基于主动运动意图的人-上肢康复机器人交互控制。在采用结点能量作为特征进行特征降维时，提出了一种伴随状态树的方法来消除结点能量的线性相关性以避免类内离散度矩阵奇异问题。降维后的特征向量输入到BP神经网络进行运动意图辨识，取得了很好的辨识效果。同时考虑虚拟现实（Virtual Reality）可使得康复训练充满娱乐性，所以基于辨识的意图动作开发了一款虚拟现实游戏来激发患者在任务关联的训练中交互的主动性和积极性。结合上肢康复机器人、肌电辨识和虚拟现实游戏进行了相关实验研究，结果验证了整个系统的可用性。
作为一种驱动器，气动肌肉因具有很强的时变和非线性特性，这使得其在实际应用中难以实现高精确控制。本文采用“三元素”模型进行了气动肌肉参数辨识和建模，并在此基础上提出了基于模糊补偿的气动肌肉滑模控制。这种控制算法在存在建模误差以及外界不确定扰动时，仍能有效提高控制精度，保证系统跟踪控制性能。此外，还提出了基于非线性干扰观测器的气动肌肉动态面控制。仿真和实际实验结果均验证了所提出算法的可行性和有效性。但基于气动肌肉建模的模型控制，对于长度、形状等不同参数的气动肌肉都需重新进行气动肌肉建模实验，这使得建模过程相对比较复杂。同时，人机动力学模型也因人而异，难以得到精确的、统一的数学模型。基于以上原因，为了解决气动肌肉在康复机器人实际应用中存在的控制精确问题，针对控制算法设计时模型未知的情况下，提出了基于ESN的PID控制算法和基于粒子群优化及ESN的单层神经网络预测控制算法。这些新提出的控制算法均能在模型未知的情况下取得很好的控制效果。
最后，对全文工作进行了总结，并提出了下一步需要改进和完善的工作。
总之，本文通过深入研究上肢康复机器人的设计及相关控制问题，为进一步实现上肢康复机器人在临床上的应用提供了一些设计方法、训练策略、控制算法等研究经验。随着研究工作的深入和完善，期待上肢康复机器人最终能转化为实际应用产品，为更多的患者提供有效的康复训练，帮助他们实现运动功能的改善和重建。
As China is entering the aging society, the stroke incidence is increasing year by year.The reconstruction and rehabilitation of motion function for the patients after stroke alsoattract the researchers’ attention. With the interdisciplinary development and integration ofthe rehabilitation medicine, mechanical design and control theory, the theory andtechnology about rehabilitation robot have been further studied and explored.
Under the support of National863project and National Natural Science Foundation,this paper developed two kinds of upper limb rehabilitation robots, which are driven bymotor and pneumatic muscle (PM), respectively. Based on the designed upper limbrehabilitation robot, with the use of Electromyography (EMG) signal, the human motionintention control based rehabilitation training is realized. In addition, based on PMmodeling, a fuzzy compensation based sliding mode control (FCBSMC) algorithm and anonlinear disturbance observer based dynamic surface control (NDOBDSC) algorithm areproposed for the high-accurate control of PM, respectively. Considering that thecomplexity of PM modeling and the dynamic model of human-upper limb rehabilitationrobot varies from individual to individual, an echo state network (ESN) based PID controlalgorithm and an ESN based single-layer neural network predictive control algorithm withparticle swarm optimization are proposed for the control of unknown model by using ESN.The main contributions focus on the following aspects:
A three degrees of freedom (DOF) upper limb rehabilitation robot driven by motor isdesigned. It can achieve shoulder extension/flexion, shoulder abduction, and elbowextension/flexion. Considering that the performance of wear, flexibility, safety andcompliance, a five DOF upper limb rehabilitation robot driven by PM is developed. It canrealize shoulder extension/flexion and rotation, elbow extension/flexion,metacarpophalangeal and proximal interphalangeal joint extension/flexion. In order toreduce the complexity of the mechanical structure and control system, PM-torsion springand PM-pull spring actuators are proposed to realize the bi-directional movement for thejoints of rehabilitation robot. This robot can realize the rehabilitation training of arm andhand at the same time, as well as be used separately. This greatly extents the availablerange for clinical application, and lays a good foundation for further clinical utility.
By collecting four-channel EMG from arm, the node energy is selected as the featureof motion intention, and the active motion intention based human-upper limbrehabilitation robot interactive control is realized. During the feature dimension reduction,a companion state tree (CST) algorithm is proposed to eliminate the linear correlation of node energy and avoid the singular problem of the within-class scatter matrix. Thereduced feature vector is fed into a BP neural network to recognize the motion intention,and the recognition result is satisfactory. Considering that virtual reality (VR) can makerehabilitation training entertaining, as a result, a VR game based on the motion intentionrecognition is designed to stimulate the patients’ interactive initiative and enthusiasm inthe task-oriented training. The relevant experiments are carried out by the combination ofthe upper limb rehabilitation robot, EMG recognition and virtual reality game to verify theavailability of the whole system.
As an actuator, PM has strong time-varying and nonlinear characteristics, whichmake it difficult to achieve high-accurate control in practical application. Thethree-element model is introduced for PM parameter identification and modeling. On thisbasis, FCBSMC is developed for PM control. This control algorithm can effectivelyimprove the control accuracy and ensure the tracking control performance in the conditionof the existence of modeling error and external uncertain disturbances. In addition,NDOBDSC is also proposed. The simulation and practical experimental results bothdemonstrate the feasibility and effectiveness of the proposed algorithm. However, themodel control based on PM modeling is required to do the PM modeling experiment again,when PM is selected with different parameters, such as length, shape and so on.Meanwhile, the whole dynamic model of human-robot depends on the conditions ofdifferent individual. It is difficult to attain an accurate and uniform mathematical model.For above reasons, to solve the problem on the high-accurate control of PM in thepractical application of rehabilitation robot, under the condition of unknown model, thispaper proposes an echo state network (ESN) based PID control algorithm and an ESNbased single-layer neural network predictive control algorithm with particle swarmoptimization, which can both obtain good control performance.
Finally, the summary of this dissertation and the future work are presented.
To sum up, the design and related control problem on upper limb rehabilitation robotare investigated deeply in this dissertation. These studies provide some researchexperience on the design methods, training strategies and control algorithms for theclinic-oriented application of the upper limb rehabilitation robot. With the furtherimprovement of the research, upper limb rehabilitation robots are expected to transforminto some practical products, which can provide effective rehabilitation training for morepatients, and improve the quality of life and realize the reconstruction of motion function.
引文
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