Nonlinear Identification and Control of Laser Welding Based on RBF Neural Networks

Abstract

A laser beam is a heat source with a high energy density; this technology has been rapidly developed and applied in the field of welding owing to its potential advantages, and supplements traditional welding techniques. An indepth analysis of its operating process could establish a good foundation for its application in China. It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process; it comprises a significant number of complex processes with random uncertain factors. Because of their nonlinear mapping and self-learning characteristics, artificial neural networks (ANNs) have certain advantages in comparison to traditional methods in the field of welding. Laser welding is a nonlinear dynamic process; these processes still pose a major challenge in the field of control. Therefore, establishing a stable model is a prerequisite for achieving accurate control. In this study, the identification and control of radial basis function neural networks in laser welding processes and self-tuning PID control methods are proposed to improve weld quality. Using a MATLAB simulation, it is shown that the proposed method can obtain a good description of the level of nonlinear dynamic control, and that the algorithm identification accuracy is high, practical, and effective. Using this method, the weld width quickly reaches the expected value and the system remains stable, with good robustness. Further, it ensures the stability and dynamic performance of the welding process and improves weld quality.

A neural network sliding mode method for nonlinear motors

“…In Equation (), Y is the network output 28–30 . w is the weight of the input items in the network to the Gauss function.…”

“…In Equation (9), Y is the network output. [28][29][30] w is the weight of the input items in the network to the Gauss function. 𝜉 is the approximation error of RBF network, usually a minimum value close to zero.…”

“…Equation (28) shows that the disturbance cancelation measure is stable. Similar to Equations ( 21) and ( 22), the compensation for external disturbance is adjusted in the same way.…”

BoT-YOLOv8: A high accuracy and stability initial weld position segmentation method for medium-thickness plate

“…After that, the welding robot was trained to perform reinforcement learning and selfsupervised learning from the weld image data, so as to realize weld feature recognition and extraction [9,10,11]. Another part of researchers established the weld feature extraction model by 3D point cloud method to complete the path planning and attitude planning of welding robots [12,13,14].…”