Journal of System Simulation
Abstract
Abstract: A six-axis robotic arm is built and simulated in a complex control environment with disturbances by using MATLAB physics engine and Python, which provides a trial-and-error environment for the robotic arm training that could not be provided in reality. Proximal policy optimization(PPO) algorithm in reinforcement learning is proposed to improve the traditional PID control algorithm. By introducing the multi-agent idea and on the basis of the different effects of the three parameters of PID on control system and the characteristics of the six-axis robotic arm, the three parameters are separately trained as different intelligent individuals to achieve a new multi-agent adaptive PID algorithm with multi-agent adaptive adjustment of parameters. Simulation results show that the algorithm outperforms MA-DDPG and MA-SAC algorithms in training convergence. Compared with the traditional PID algorithm, the algorithm can effectively suppress the disturbances and oscillations, and has lower overshoot and adjustment time, which makes the control process smoother and effectively improves the control accuracy of the robotic arm. The robustness and effectiveness is proved.
Recommended Citation
Zhou, Zhiyong; Mo, Fei; Zhao, Kai; Hao, Yunbo; and Qian, Yufeng
(2024)
"Adaptive PID Control Algorithm Based on PPO,"
Journal of System Simulation: Vol. 36:
Iss.
6, Article 15.
DOI: 10.16182/j.issn1004731x.joss.23-0137
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss6/15
First Page
1425
Last Page
1432
CLC
TP242.2
Recommended Citation
Zhou Zhiyong, Mo Fei, Zhao Kai, et al. Adaptive PID Control Algorithm Based on PPO[J]. Journal of System Simulation, 2024, 36(6): 1425-1432.
DOI
10.16182/j.issn1004731x.joss.23-0137
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