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基于神经网络和遗传算法的下肢康复 2 自由度机器人优化智能控制。

Optimized intelligent control of a 2-degree of freedom robot for rehabilitation of lower limbs using neural network and genetic algorithm.

机构信息

Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

出版信息

J Neuroeng Rehabil. 2013 Aug 14;10:96. doi: 10.1186/1743-0003-10-96.

DOI:10.1186/1743-0003-10-96
PMID:23945420
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3751752/
Abstract

BACKGROUND

There is an increasing trend in using robots for medical purposes. One specific area is rehabilitation. Rehabilitation is one of the non-drug treatments in community health which means the restoration of the abilities to maximize independence. It is a prolonged work and costly labor. On the other hand, by using the flexible and efficient robots in rehabilitation area, this process will be more useful for handicapped patients.

METHODS

In this study, a rule-based intelligent control methodology is proposed to mimic the behavior of a healthy limb in a satisfactory way by a 2-DOF planar robot. Inverse kinematic of the planar robot will be solved by neural networks and control parameters will be optimized by genetic algorithm, as rehabilitation progress.

RESULTS

The results of simulations are presented by defining a physiotherapy simple mode on desired trajectory. MATLAB/Simulink is used for simulations. The system is capable of learning the action of the physiotherapist for each patient and imitating this behaviour in the absence of a physiotherapist that can be called robotherapy.

CONCLUSIONS

In this study, a therapeutic exercise planar 2-DOF robot is designed and controlled for lower-limb rehabilitation. The robot manipulator is controlled by combination of hybrid and adaptive controls. Some safety factors and stability constraints are defined and obtained. The robot is stopped when the safety factors are not satisfied. Kinematics of robot is estimated by an MLP neural network and proper control parameters are achieved using GA optimization.

摘要

背景

机器人在医学领域的应用呈上升趋势。其中一个特定领域是康复。康复是社区卫生中的非药物治疗之一，意味着最大限度地恢复自理能力。这是一个漫长而昂贵的过程。另一方面，通过在康复领域使用灵活高效的机器人，这一过程将对残疾患者更有帮助。

方法

在这项研究中，提出了一种基于规则的智能控制方法，通过 2-DOF 平面机器人以令人满意的方式模拟健康肢体的行为。平面机器人的逆运动学将通过神经网络求解，控制参数将通过遗传算法优化，以适应康复进度。

结果

通过在期望轨迹上定义物理治疗简单模式，展示了模拟的结果。MATLAB/Simulink 用于模拟。该系统能够为每个患者学习物理治疗师的动作，并在没有物理治疗师的情况下模仿这种行为，这可以称为机器人治疗。

结论

在这项研究中，设计并控制了一种用于下肢康复的治疗性运动平面 2-DOF 机器人。机器人操纵器采用混合和自适应控制相结合的方式进行控制。定义并获得了一些安全因素和稳定性约束。当安全因素不满足时，机器人将停止。机器人的运动学通过 MLP 神经网络进行估计，并使用 GA 优化获得适当的控制参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/d466d6f6e6d4/1743-0003-10-96-10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/6fb75653ff3a/1743-0003-10-96-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/cae629582033/1743-0003-10-96-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/b5ee54bdb36d/1743-0003-10-96-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/b38900fe441f/1743-0003-10-96-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/61556e4212e6/1743-0003-10-96-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/6b8963d2c50e/1743-0003-10-96-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/d195b3063ac9/1743-0003-10-96-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/b6b1cb57c37d/1743-0003-10-96-9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/d466d6f6e6d4/1743-0003-10-96-10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/6fb75653ff3a/1743-0003-10-96-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/cae629582033/1743-0003-10-96-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/b5ee54bdb36d/1743-0003-10-96-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/b38900fe441f/1743-0003-10-96-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/61556e4212e6/1743-0003-10-96-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/6b8963d2c50e/1743-0003-10-96-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/d195b3063ac9/1743-0003-10-96-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/b6b1cb57c37d/1743-0003-10-96-9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6248/3751752/d466d6f6e6d4/1743-0003-10-96-10.jpg

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Patient-cooperative strategies for robot-aided treadmill training: first experimental results.机器人辅助跑步机训练的患者合作策略：首次实验结果
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