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Multi-function robot system in physical education teaching under big data environment
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AI
DOI：10.1007/s00500-023-08356-w.png)
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Abstract
En 中文
Physical exercise (PE) and competitive sports have long cycles, a high risk of injury, and obvious defects in passive physical training and physical exercise characteristics. Therefore, this paper designs a PE teaching system based on Partical Swarm Optimization (PSO) and Fuzzy Proportional-Integral-Derivative (PID) control. To capture PE teaching information, the system embeds maker induction equipment into the structure of the sports robot. To overcome the PID control algorithm's problems of poor flexibility resistance and low position control accuracy, adaptive neurons are added to optimize, and PSO is used to optimize parameters. The simulation results show that the response time of the PSO-Fuzzy-PID is reduced by 34%, the overshoot is reduced by 17%, and the adjustment time is shortened by 65% compared with the traditional PID. In different teaching cycles, the teaching coverage rate of the system reaches 97.52%, which provides a reference for the application mode of sports robots and its organic integration of artificial intelligence technology.
Keywords:
Sports robot
Fuzzy PID
PSO
PE teaching
Journal
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2.5
Papers:
1.0W
Citations:
2.1W