In order to realize the pressure control of the tractor electronic hydraulic hitch in the fields, the pressure control algorithm is essential. In this study, combining the kinematics model with the dynamic model of ploughing and the hydraulic system model, an adaptive fuzzy PID controller is proposed to adjust the real-time data of the PID parameters for the pressure control of the tractor electronic hydraulic hitch. The feasibility of the proposed controller was verified by simulation. Next, a pressure control experimental with the real vehicle experiment platform was carried out under three control algorithms of the traditional PID, the traditional PID with compensation correction and the adaptive fuzzy PID with compensation correction in verifying the pressure control effect of the tractor in different controllers. When the system was stable, the experimental results showed that the input was 1.5 MPa step signal with response time in the traditional PID controller of 2.5 s, fluctuation range of 0.5 MPa. However, the response time in the adaptive fuzzy PID with compensation correction was 1.5 s, fluctuation range of 0.3 MPa. The responding time was 40% lower, and the pressure fluctuation range was reduced by 40%. In conclusion, the proposed algorithm successfully realized the pressure control of the tractor. The proposed adaptive fuzzy PID with compensation correction in this paper has a better dynamic performance.
The drilling robot is the key equipment for pressure relief in rockburst mines, and the accurate recognition of a pressure relief hole is the premise for optimizing the layout of pressure relief holes and intelligent drilling. In view of this, a pressure relief hole recognition method for a drilling robot, based on single-image generative adversarial network (SinGAN) and improved faster region convolution neural network (Faster R-CNN), is proposed. Aiming at the problem of insufficient sample generation diversity and poor performance of the traditional SinGAN model, some improvement measures including image size adjustment, multi-stage training, and dynamically changing iteration times are designed as an improved SinGAN for the generation of pressure relief hole images. In addition, to solve the problem that the traditional depth neural network is not ideal for small-size target recognition, an improved Faster R-CNN based on multi-scale image input and multi-layer feature fusion is designed with the improved SqueezeNet as the framework, and the sample data collected from ground experiments are used for comparative analysis. The results indicate that the improved SinGAN model can improve the diversity of generated images on the premise of ensuring the quality of image samples, and can greatly improve the training speed of the model. The accuracy and recall rate of the improved Faster R-CNN model were able to reach 90.09% and 98.32%, respectively, and the average detection time was 0.19 s, which verifies the superiority of the improved Faster R-CNN model. To further verify the practicability of the proposed method, some field images were collected from the underground rockburst relief area in the coal mine, and a corresponding test analysis was carried out. Compared with three YOLO models, the accuracy and recall rate of improved Faster R-CNN model improved significantly, although the training time and recognition time increased to a certain extent, which proves the feasibility and effectiveness of the proposed method.
Product design-oriented models are a decision-making problem with multiple criteria based on the assessment of product design factor that represent engineer judgments and customer desires, and are used to adapt to increasing competition and high levels of customization. Thus, in this work, a “sandwich-like” architecture and a hybrid integrated approach including triangle fuzzy numbers, quality function deployment, and the LeaderRank algorithm are respectively proposed to express and assess product designs more effectively. Additionally, the proposed approach is a measure study which can determine the design requirements, the relationships between product design and customer requirements, and the correlations among product values. The fuzzy analytical network process and LeaderRank algorithm are employed to evaluate the product design requirements and interaction between the product value and the uncertainty that exists in expert judgment. Besides, a case study is selected as an example to illustrate the “sandwich-like” architecture in the product design stage, and a priority analysis is performed to assess the importance degrees of product designs for customer needs, thereby providing optimization for product design and an effective approach to improve the product value.
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