Research on Control Strategy Technology of Upper Limb Exoskeleton Robots: Review
Upper limb exoskeleton robots, as highly integrated wearable devices with the human body structure, hold significant potential in rehabilitation medicine, human performance enhancement, and occupational safety and health. The rapid advancement of high-precision, low-noise acquisition devices and int...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/3/207 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Upper limb exoskeleton robots, as highly integrated wearable devices with the human body structure, hold significant potential in rehabilitation medicine, human performance enhancement, and occupational safety and health. The rapid advancement of high-precision, low-noise acquisition devices and intelligent motion intention recognition algorithms has led to a growing demand for more rational and reliable control strategies. Consequently, the control systems and strategies of exoskeleton robots are becoming increasingly prominent. This paper innovatively takes the hierarchical control system of exoskeleton robots as the entry point and comprehensively compares the current control strategies and intelligent technologies for upper limb exoskeleton robots, analyzing their applicable scenarios and limitations. The current research still faces challenges such as the insufficient real-time performance of algorithms and limited individualized adaptation capabilities. It is recognized that no single traditional control algorithm can fully meet the intelligent interaction requirements between exoskeletons and the human body. The integration of many advanced artificial intelligence algorithms into intelligent control systems remains restricted. Meanwhile, the quality of control is closely related to the perception and decision-making system. Therefore, the combination of multi-source information fusion and cooperative control methods is expected to enhance efficient human–robot interaction and personalized rehabilitation. Transfer learning and edge computing technologies are expected to enable lightweight deployment, ultimately improving the work efficiency and quality of life of end-users. |
|---|---|
| ISSN: | 2075-1702 |