Self-tuning fuzzy PID control of manipulator grasping force based on improved particle swarm optimization algorithm
To improve the stability of underactuated robotic arms in the sorting process of fragile parts, a fuzzy PID control algorithm is proposed to optimize their grasping performance by improving the particle swarm algorithm. Firstly, the characteristics of the underactuated robotic arm grasping force con...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of XPU
2024-08-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
| Subjects: | |
| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1486 |
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| Summary: | To improve the stability of underactuated robotic arms in the sorting process of fragile parts, a fuzzy PID control algorithm is proposed to optimize their grasping performance by improving the particle swarm algorithm. Firstly, the characteristics of the underactuated robotic arm grasping force control system were analyzed and a specific strategy was proposed that combines particle swarm algorithm optimization algorithm with fuzzy PID grasping force control system. Secondly, methods such as dynamic inertia weights were introduced into particle swarm algorithm to improve its iteration speed and avoid falling into local optima. On this basis, the improved particle swarm optimization algorithm was used to optimize the relevant parameters of the fuzzy PID controller, achieving online self-tuning of fuzzy rule weights and quantization factors, and solving the problem of PID parameters unable to be dynamically adjusted. Finally, the method was simulated and analyzed. The results show that the control algorithm designed in this paper can achieve stable grasping force within 0.8 s, with a steady-state error of less than 0.2%, and a disturbance tuning time of 0.262 s. The transient response speed, control accuracy, and stability of the system are significantly improved. |
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| ISSN: | 1674-649X |