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Constant force grinding controller for robots based on SAC optimal parameter finding algorithm.
Rei, Chosei; Wang, Qichao; Chen, Linlin; Yan, Xinhua; Zhang, Peng; Fu, Liwei; Wang, Chong; Liu, Xinghui.
Afiliación
  • Rei C; Nobot Intelligent Equipment (Shandong) Co., Ltd, Liaocheng, Shandong, China.
  • Wang Q; School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, Shandong, China.
  • Chen L; School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, Shandong, China. chenlinlin@lcu.edu.cn.
  • Yan X; Nobot Intelligent Equipment (Shandong) Co., Ltd, Liaocheng, Shandong, China.
  • Zhang P; Nobot Intelligent Equipment (Shandong) Co., Ltd, Liaocheng, Shandong, China.
  • Fu L; Nobot Intelligent Equipment (Shandong) Co., Ltd, Liaocheng, Shandong, China.
  • Wang C; School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, Shandong, China.
  • Liu X; Department of Materials Physics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMTS), Chennai, Tamilnadu, India. liuxinghui119@gmail.com.
Sci Rep ; 14(1): 14127, 2024 Jun 19.
Article en En | MEDLINE | ID: mdl-38898063
ABSTRACT
Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido