【摘要】 传统激光软钎焊依赖人工经验调整参数，难以应对复杂产品结构和温度动态变化，基于模型的方法又高度依赖模型精度，实用性受限.为解决上述问题，文中提出了一种基于迭代学习的激光软钎焊温度控制方法.无需建立精确数学模型，利用历史焊接数据进行迭代优化，逐步修正控制功率，使实际温度曲线在无需人工干预的情况下逐步逼近目标温度曲线，从而增强控制系统的自适应能力与鲁棒性.结果表明，仅需2次迭代，在实际温度曲线与目标温度曲线的均方根误差和最大绝对误差方面，即优于传统PID(proportional integral derivative)控制方法.迭代3次后，温度控制的精度、稳定性和工艺适应性均得到显著提升，表现出较强的收敛性与控制性能.为激光软钎焊在多变复杂工况下实现高精度、高一致性的温度控制提供了有效解决思路.更多还原

【Abstract】 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.更多还原