The identification and control of a finger exoskeleton for grasping rehabilitation
This paper evaluates the efficacy of different classical control architectures in performing grasping motion. The exoskeleton system was obtained via system identification method in which the input and output data was measured by means of current sensor (ACS712) and encoder attached to a DC geared m...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Book Section |
| Language: | English English |
| Published: |
Springer Singapore
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21163/ http://umpir.ump.edu.my/id/eprint/21163/ http://umpir.ump.edu.my/id/eprint/21163/ http://umpir.ump.edu.my/id/eprint/21163/7/Identification%20and%20Control%20of%20a%20finger%20exoskeleton-fkp-2018-1.pdf http://umpir.ump.edu.my/id/eprint/21163/13/book41%20The%20identification%20and%20control%20of%20a%20finger%20exoskeleton%20for%20grasping%20rehabilitation.pdf |
| Summary: | This paper evaluates the efficacy of different classical control architectures in performing grasping motion. The exoskeleton system was obtained via system identification method in which the input and output data was measured by means of current sensor (ACS712) and encoder attached to a DC geared motor (SPG30e-270k). The data obtained is split with a ratio of 70:30 for estimation and validation, respectively. The transfer function of the system is evaluated by varying the number of poles and zeros that are able to fit well with validation data. The performance of the classical P, PI, PD and PID control techniques were then evaluated in its ability to track the desired trajectory. It was demonstrated from the study that the PID controller provides the least steady state error as well as a reasonably fast settling time. |
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