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Addressing input saturation, external disturbances, and uncertain system parameters, this paper investigates the position tracking control problem for bilateral teleoperation systems with a time delay communication channel. Based on a composite energy function, we propose an adaptive iterative learning control (AILC) method to achieve the objective of position tracking under the alignment condition. This extends the existing research on the control of nonlinear teleoperation systems with time delay. The saturation constraint property of the Softsign function ensures that no state of the system exceeds its constraints. The controller learns to simultaneously deal with the uncertainty of system parameters online, reject external disturbances, and eliminate positional errors along the time and iteration axes. All signals in the system for any constant time delay are proved to be bounded. Ultimately, the performance of the proposed controller is further verified through numerical simulations. [ABSTRACT FROM AUTHOR]

In order to solve the problem of variable steady-state operation nodes and poor coordination control effect in photovoltaic energy storage plants, the coordination control strategy of photovoltaic energy storage plants based on ADP is studied. Establish the photovoltaic energy storage power station model including photovoltaic system model, super capacitor system model and battery system model; Set the maximum limit of active power change as the power constraint condition for coordinated control of photovoltaic energy storage station; The optimal control problem of multi voltage and reactive power resource coordination is fully considered, the optimal voltage control model is established by using ADP algorithm, and the optimal coordinated control strategy is obtained by online learning the collected dynamic operation information. The experimental results show that this strategy can improve the coordinated control effect of the photovoltaic energy storage station, ensure the photovoltaic energy storage station in a stable operation state, improve the service life of the energy storage device in the photovoltaic energy storage station, and stabilize the steady-state operating node voltage within the ideal fluctuation range. [ABSTRACT FROM AUTHOR]

To address the high complexity and low accuracy issues of traditional methods in mixed coal vitrinite identification, this paper proposes a method based on an improved DeepLabv3+ network. First, MobileNetV2 is used as the backbone network to reduce the number of parameters. Second, an atrous convolution layer with a dilation rate of 24 is added to the ASPP (atrous spatial pyramid pooling) module to further increase the receptive field. Meanwhile, a CBAM (convolutional block attention module) attention mechanism with a channel multiplier of 8 is introduced at the output part of the ASPP module to better filter out important semantic features. Then, a corrective convolution module is added to the network's output to ensure the consistency of each channel's output feature map for each type of vitrinite. Finally, images of 14 single vitrinite components are used as training samples for network training, and a validation set is used for identification testing. The results show that the improved DeepLabv3+ achieves 6.14% and 3.68% improvements in MIOU (mean intersection over union) and MPA (mean pixel accuracy), respectively, compared to the original DeepLabv3+; 12% and 5.3% improvements compared to U-Net; 9.26% and 4.73% improvements compared to PSPNet with ResNet as the backbone; 5.4% and 9.34% improvements compared to PSPNet with MobileNetV2 as the backbone; and 6.46% and 9.05% improvements compared to HRNet. Additionally, the improved ASPP module increases MIOU and MPA by 3.23% and 1.93%, respectively, compared to the original module. The CBAM attention mechanism with a channel multiplier of 8 improves MIOU and MPA by 1.97% and 1.72%, respectively, compared to the original channel multiplier of 16. The data indicate that the proposed identification method significantly improves recognition accuracy and can be effectively applied to mixed coal vitrinite identification. [ABSTRACT FROM AUTHOR]

Aiming to address the A* algorithm's issues of traversing a large number of nodes, long search times, and large turning angles in path planning, a strategy for multiple improvements to the A* algorithm is proposed. Firstly, the calculation of the heuristic function is refined by utilizing the Octile distance instead of traditional distance, which more accurately predicts the optimal path length. Additionally, environmental constraints are introduced to adaptively adjust the weight of the heuristic function, balancing the trade-off between search speed and path length. Secondly, the bidirectional jump point search method is integrated, allowing simultaneous path searches from both directions. This significantly reduces path search times and the number of nodes traversed. Finally, the path undergoes two rounds of smoothing using a path smoothing strategy until the final path is generated. To validate the effectiveness of the improved A* algorithm, simulations are conducted on ten types of grid maps. Results demonstrate that the improved A* algorithm markedly decreases path search times while maintaining path length, with greater speed improvements observed as the map size increases. Furthermore, the improved algorithm is applied in experiments with mobile robots, achieving significant reductions in average path search times of 79.04% and 37.41% compared to the traditional A* algorithm and the JPS algorithm, respectively. This enhancement effectively meets the requirements for rapid path planning in mobile robotics applications. [ABSTRACT FROM AUTHOR]

The sleeve grouting connection is the most common form of vertical connection for prefabricated shear walls. However, during construction, this type of connection is prone to defects such as insufficient anchorage length of reinforcement, deviation of reinforcement, and insufficient amount of sleeve grouting, which significantly impact the integrity and seismic performance of the prefabricated shear wall structure. The finite element analysis of the prefabricated shear wall with sleeve grouting connection is still based on solid element modeling. This method has the disadvantages of complex models and low computational efficiency. In this paper, a simplified modeling method for prefabricated shear walls considering sleeve grouting defects was proposed to address this issue. Firstly, the equivalent constitutive relationship of the sleeve grouting defect connector was constructed based on the uniaxial tensile test of the existing sleeve grouting defect connector, and the T3D2 element was used to simulate the sleeve grouting connector. Then, the mechanical behavior of the horizontal joint between the shear wall and the foundation beam was simulated by the cohesive force model, and the prefabricated shear wall models with sleeve grouting defects were established. The accuracy of the simplified modeling method was verified by comparing the experimental results and numerical simulation results of the seismic performance of the prefabricated shear wall with sleeve grouting defects. The results showed that the hysteresis curve, skeleton curve, and failure mode of the numerical simulation were in good agreement with the test results. However, the stiffness of the concrete degenerated rapidly due to the apparent development of plastic-damaged concrete, which made the falling section of the hysteresis curve of the numerical simulation different from that of the test. The proposed simplified modeling method can be further applied to the performance study of prefabricated shear walls with sleeve grouting defects and can provide a reference for structural performance evaluation, design optimization, and construction quality control to a certain extent. [ABSTRACT FROM AUTHOR]