Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

Barhaghtalab, Mojtaba Hadi; Sepestanaki, Mohammadreza Askari; Mobayen, Saleh; Jalilvand, Abolfazl; Fekih, Afef; Meigoli, Vahid

doi:10.1016/j.asoc.2023.110970

Cited by 3 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, with the continuous development of artificial intelligence technology, artificial intelligence technology has been widely used in various fields. For example, in order to reduce the error of the control system, Barhaghtalab et al proposed a control method of six-DOF robotic arm based on adaptive fuzzy neural reasoning system (Barhaghtalab et al, 2023). In addition, Chowdhury's team proposed an entropymaximized double delay depth deterministic strategy gradient automatic PID tuning method to solve the problem of poor PID tuning method (Chowdhury et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control

Ma,

2024

Front. Mech. Eng.

View full text Add to dashboard Cite

Introduction: Modern industrial manufacturing often requires the eight-bar stamping mechanism to have high motion accuracy and stability. To meet these stringent requirements, traditional control techniques such as proportional-integral-derivative (PID) control need to be improved.Methods: In this study, radial basis function neural network is introduced to improve the traditional proportional integral derivative control technique. The improved proportional integral derivative technique is applied to the modeling and optimization of eight kinds of bar stamping mechanisms.Results: Comparing the improved control technology, the experiment showed that the peak time and adjustment time of the improved technology were 0.516 s and 1.038 s, respectively, which are better than the comparative control technology. In addition, in the comparative analysis of the eight bar stamping mechanism, the proposed architecture scored 9.3 points in operational efficiency, which is significantly greater than the comparative architecture.Discussion: The results show that the combination of PID control strategy and radial basis function neural network provides a powerful tool for dynamic modeling and optimization of eight-bar stamping mechanism. It not only provides enhanced motion accuracy and stability, but also brings significant practicality to industrial manufacturing. This integration opens up new possibilities for improving the performance of complex mechanical systems to meet the evolving needs of modern manufacturing.

show abstract

Section: Introductionmentioning

confidence: 99%