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 in-depth 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.