Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Çağatay Ersin, Mustafa Yaz
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Abstract

In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy logic algorithm. The same data were then trained with machine learning algorithms in Simulink software. It has been determined that the best result is the quadratic support vector machine (SVM) algorithm. The fuzzy logic algorithm trained with the PID controller block and the received sensor data have been added to the degrees of freedom regions that will enable rotation in the block diagram of the previously exported system. Later, the fuzzy logic block was removed and the machine learning algorithm, the quadratic SVM algorithm, was added. The designed system was operated with two different control systems, and the control algorithm closest to the human arm movement was determined. In addition, each part of the system, whose design was prepared, was removed and assembled separately with a 3D printer. ESP32 microcontroller development board was used to control the system, and it was run in real-time with EMG muscle sensors and position sensors.
利用模糊逻辑和机器学习控制实现和比较可穿戴外骨骼手臂设计
本研究设计了一种可穿戴外骨骼机械臂,并采用不同的控制方法对其进行控制,以帮助手臂肌肉有障碍的人并支持治疗。开发的机械臂通过 Simmechanics 应用程序转移到 Simulink 软件中。两个肌电图(EMG)肌肉传感器和 ADXL335 位置与加速度传感器分别安装在人体手臂的肱二头肌和肱三头肌部位。当人移动手臂时,EMG 肌肉传感器和 ADXL335 位置与加速度传感器会获取数据。首先用模糊逻辑算法对接收到的数据进行训练。然后用 Simulink 软件中的机器学习算法训练相同的数据。结果表明,二次支持向量机(SVM)算法效果最佳。使用 PID 控制器块训练的模糊逻辑算法和接收到的传感器数据已被添加到自由度区域,从而可以在先前导出的系统框图中进行旋转。之后,模糊逻辑块被移除,并添加了机器学习算法,即二次 SVM 算法。设计的系统通过两种不同的控制系统运行,并确定了最接近人类手臂运动的控制算法。此外,还将设计好的系统各部分拆卸下来,用 3D 打印机分别组装。使用 ESP32 微控制器开发板控制该系统,并通过 EMG 肌肉传感器和位置传感器实时运行。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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