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A Simple OpenSim-Simulink Interface for Cascaded Zero-Force Control of Human-Robot Interaction in a Hip Exoskeleton Robot

外骨骼 机器人 计算机科学 接口(物质) 控制器(灌溉) 模拟 控制工程 工程类 人工智能 农学 生物 最大气泡压力法 气泡 并行计算
作者
SeyedHooman Hosseini-Zahraei,Mohadeseh Shafie Tali,Mersad Hafifeh Saberi,Iman Kardan,Alireza Akbarzadeh
标识
DOI:10.1109/icrom57054.2022.10025309
摘要

OpenSim-Simulink interfaces are increasingly used as powerful forward-dynamic tools for the design, implementation, and performance evaluation of different human-centered devices. This paper proposes a new OpenSim-Simulink interface, with a simple programming procedure, for the zero-force control of human-exoskeleton interactions. A skeletal model of the human body and a CAD model of a custom-made hip exoskeleton is developed in the OpenSim Software. The Torso link of the robot is rigidly attached to the human pelvis. At the same time, bushing forces are used to develop a compliant contact between the link of the robot and the human thigh. The hip joints of the robot are aligned with the corresponding human joints, and coordinate actuators are used to actuate the extension/flexion DOF of the robot’s hip. Then, establishing the simple OpenSim-Simulink interface, some algorithms are designed in Simulink to control the human motions and to adjust the human-robot interaction forces. In this paper, two different force control methods are designed to minimize the human-robot interaction forces. This first method is a fuzzy controller with force and joint angle feedback, and the second method is a cascaded controller with two feedback loops of the force error and the force derivative. The developed OpenSim-Simulink interface is used to implement the designed force controllers and compare their performances. The results verify that by using the designed controllers, the robot successfully tracks the human leg motion, and the interaction force is effectively reduced. The results also indicate the outperformance of the cascaded controller with an 86.66% reduction in the peak interaction force compared to the 80% reduction achieved by the fuzzy controller.

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