Fei Zhang

Parameter tuning for Active Disturbance Rejection Control (ADRC) in rolling mill hydraulic synchronization systems is critical for enhancing strip quality. Conventional manual trial-and-error methods often yield suboptimal results. This paper proposes a hybrid algorithm, WMA-PSO, integrating the Humpback Whale Migration Algorithm (WMA) with Particle Swarm Optimization (PSO) through an adaptive fusion weight strategy. This approach effectively balances global exploration and local exploitation, improving optimization accuracy and efficiency. Evaluation on the CEC-2005 benchmark suite shows that WMA-PSO outperforms several state-of-the-art algorithms. Simulation experiments on ADRC tuning in a rolling mill system demonstrate that the WMA-PSO-optimized controller achieves the smallest synchronization error and superior overall control performance compared to other methods. The results validate WMA-PSO as an effective tool for automated parameter tuning in complex industrial control systems.

In the process of high-speed wire rod production, flying steel accidents may occur due to various reasons. Current detection methods relying on sensors like hardware make debugging complex as well as limit real-time and accuracy. These methods are complicated to debug, and the real-time and accuracy of detection are poor. Therefore, this paper proposes a flying steel detection method based on improved You Only Look Once v8 (YOLOv8), which can realize high-precision flying steel detection based on machine vision through the monitoring video of the production site. Firstly, the Omni-dimensional Dynamic Convolution (ODConv) is added to the backbone network to improve the feature extraction ability of the input image. Then, a lightweight C2f-PCCA_RVB module is proposed to be integrated into the neck network, so as to carry out the lightweight design of the neck network. Finally, the Efficient Multi-Scale Attention (EMA) module is added to the neck network to fuse the context information of different scales and improve the feature extraction ability. The experimental results show that the average accuracy (mAP@0.5) of the flying steel detection method based on the improved YOLOv8 is 99.1%, and the latency is reduced to 2.5 ms, which can realize the real-time accurate detection of the flying steel.

In traditional centralized steel plant production monitoring systems, there are two major problems. On the one hand, the limited shooting angles of cameras make it impossible to capture comprehensive information. On the other hand, using multiple cameras to display monitoring screens separately on a large screen leads to clutter and easy omission of key information. To address the above-mentioned issues, this paper proposes an image stitching technique based on an improved LightGlue algorithm. First of all, this paper employs the SuperPoint (Self-Supervised Interest Point Detection and Description) algorithm as the feature extraction algorithm. The experimental results show that this algorithm outperforms traditional algorithms both in terms of feature extraction speed and extraction accuracy. Then, the LightGlue (Local Feature Matching at Light Speed) algorithm is selected as the feature matching algorithm, and it is optimized and improved by combining it with the Agglomerative Clustering (AGG) algorithm. The experimental results indicate that this improvement effectively increases the speed of feature matching. Compared with the original LightGlue algorithm, the matching efficiency is improved by 26.2%. Finally, aiming at the problems of parallax and ghosting existing in the image fusion process, this paper proposes a pixel dynamic adaptive fusion strategy. A local homography matrix strategy is proposed in the geometric alignment stage, and a pixel difference fusion strategy is proposed in the pixel fusion stage. The experimental results show that this improvement successfully solves the problems of parallax and ghosting and achieves a good image stitching effect.

In the control system of three-phase voltage source pulse width modulation rectifier, dynamic control of direct current bus voltage has become the research focus. This paper proposes an active disturbance rejection control- model predictive direct power control strategy, without a bus voltage sensor, aiming at improving the fault tolerance of the direct current side. This strategy considers the dynamic response ability of rectifier operation without a voltage sensor and avoids the outer loop feedback distortion caused by voltage sensor faults. Model predictive direct power control is used in the inner loop. For the improvement of system response performance, bus voltage extended state observer and active disturbance rejection controller are designed for the outer loop. In addition, error differential term is added to the extended state observer to realize fast tracking of dynamic voltage without sensor. An adaptive threshold is designed by comparing the residual error and threshold, which realizes the fast switching and system normal operation under voltage sensor fault. The proposed control strategy is verified by simulation and experiment. The results show that the observation error of the proposed strategy is within 2V, which is down about 2V from direct power control and model predictive direct power control. When the bus voltage step, the observed voltage fluctuation is 5V, and the adjustment time is about 75ms. Combined with the strategy, extended state observer can well track the dynamic change of bus voltage and realizes automatic switching control under sensor fault.

To solve the problem of unsatisfactory control and poor real-time performance of nonlinear time-varying multi-input systems, this article proposes an intelligent model predictive control (MPC) algorithm inspired by heuristic dynamic programming (HDP), biological control theory, and operations research. Considering that the internal feedback information from a neural network (NN) is low, a multilevel feedback NN is proposed. Combining an NN with a biofeedback mechanism increases the internal feedback information and improves the convergence accuracy of the NN. The multilevel feedback network is used in three internal networks of the intelligent MPC algorithm. In order to improve the convergence speed of the proposed algorithm, a biologically inspired central coordination module and operations research theory inspired priority factor module is incorporated within the HDP algorithm. The prediction accuracy and control speed of the algorithm for nonlinear time-varying systems is greatly improved without affecting the control accuracy. The stability and convergence of the intelligent MPC algorithm is demonstrated on test data. Finally, the effectiveness and superiority of the proposed MPC algorithm is verified and compared against several traditional algorithms.

The scrap falling state in the shearing process of a Wide Heavy Plate Mill depends on manual monitoring and judgment. The steel plate can only move forward after the scrap falls, otherwise, the equipment will be damaged. An experienced operator can use the sound they hear to estimate the falling state. To automatically recognize sound events, a scheme of sound data acquisition processing and recognition for monitoring the scrap state of the shearing process is proposed in this paper. Firstly, a pickup is installed at the monitoring point of the crop shear to collect the sound. Afterward, the sound data is processed and finally fed into the model for recognition. Since many sound segments in the field are not related to key actions, an endpoint detection technique based on a short-term energy threshold and the variable window length strategy is applied in the data processing section to obtain the sound segments to be identified. The Log-Mel spectrogram feature of the sound segment is then extracted. After a data filling to ensure a uniform data shape, the feature map is fed into a lightweight deep convolution neural network called MobileNetV2 to quickly identify sound events. For the shearing process events, the model recognition accuracy can reach 95% of the F1 score in a realistic noise environment and we can identify the scrap falling 0.3s after the action starts. Finally, the model was applied to the UrbanSound8K dataset and achieved 95% recognition accuracy, validating the performance of the method.