Temperature control for laser soldering based on iterative learning

Traditional laser soldering typically relies on manual experience to adjust control parameters, which makes it difficult to cope with complex product structures and dynamic temperature variations. Model-based approaches are overly dependent on the accuracy of the model itself, which significantly limits their practical applicability. To address these challenges, a laser soldering temperature control method based on iterative learning was proposed. Instead of establishing an accurate mathematical model, the proposed approach utilized historical welding data for iterative optimization. By gradually adjusting the control power, it enabled the actual temperature curves to closely approach the target temperature curve without the need for human intervention, thereby enhancing the adaptability and robustness of the control system. The results show that after only two iterations, the proposed method outperforms traditional proportional integral derivative (PID) control in terms of both the standard deviation and the maximum error between the actual temperature curves and target temperature curves. After three iterations, the method achieves significant improvements in temperature control accuracy, stability, and adaptability to different process conditions, exhibiting strong convergence and control performance. This method provides an effective solution for achieving high-precision and highly consistent temperature control in laser soldering under complex and variable working conditions.