Seam tracking with structured light vision has been widely applied into the robot welding. And the precise laser stripe extraction is the premise of automatic laser vision seam tracking. However, conventional laser stripe extraction methods based on image processing have the shortcomings of poor flexibility and robustness, which are easily affected by considerable image noises in the welding processing, such as arc light, smoke, and splash. To address this issue, inspired by image segmentation, with the strong contextual feature expression ability of deep convolutional neural network (DCNN), a novel image denoising method of seam images is proposed in this paper for automatic laser stripe extraction to serve intelligent robot welding applications, such as seam tracking, seam type detection, weld bead detection, etc. With the deep encoder-decoder network framework, aimed at the information loss issue by multiple convolutional and pooling operations in DCNNs, an attention dense convolutional block is proposed to extract and accumulate multi-scale feature maps. Meanwhile, a residual bi-directional ConvLSTM block (BiConvLSTM) is proposed to effectively learn multi-scale and long-range spatial contexts from local feature maps. Finally, a weighted loss function is proposed for model training to address the class unbalanced issue. Combined with the seam image set, the experimental results show that the proposed image denoising network could correctly extract the laser stripes from seam images which could demonstrate that the proposed method shows a high detection precision and good robustness against the strong image noise interference from welding process.

Several improved active disturbance rejection control (ADRC) methods, including input modified ADRC (MADRC), input delay ADRC, Smith predictor-based ADRC, and Smith-like based ADRC (SLADRC), have been proposed to tackle $\boldsymbol{K/(Ts+1)^{n}}$ -type high-order processes. This article aims to investigate their control performance theoretically, such as the input disturbance rejection and measurement noise rejection capabilities. It is proved that these improved ADRCs with the same parameters have the same measurement noise rejection capability. Besides, quantitative tuning rules of MADRC and SLADRC are derived based on their desired dynamic equations. Compared with proportional-integral controller and regular ADRC, their effectiveness is verified by comparative simulations and experiments, where MADRC obtains the best control performance with good robustness. Finally, the successful field applications of MADRC on the main steam pressure process further illustrate its application potential in complicated industrial processes.