Robotics has rapidly advanced, revolutionizing manufacturing, healthcare, agriculture, and logistics industries. These advances have enabled robots to perform increasingly complex tasks with greater efficiency and adaptability. However, robotic control remains a major challenge due to robotic systems' complex dynamics, nonlinearities, and uncertainties. Traditional control methods often rely on accurate mathematical models, which are difficult to obtain for complex robots. Data-driven model predictive control (DD-MPC) is a promising solution that overcomes the limitations of traditional methods by leveraging data to learn system dynamics. Unlike model-free methods that lack safety guarantees or model-based methods that struggle with complexity, DD-MPC offers a balance between flexibility and performance. It facilitates real-time optimization, adeptly manages multifaceted constraints, and exhibits adaptability to spatiotemporal dynamic changes. This survey explores the application of DD-MPC in robotic control, highlighting its advantages over other control strategies and its potential to address current challenges in the field.

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The identification of modulation mode constitutes a pivotal element within satellite communication systems. Its utilization is pervasive, manifesting in domains such as signal demodulation, resource allocation, and communication quality assessment. However, traditional methods of modulation mode identification are dependent on manual feature extraction, which is both time-consuming and less adaptable in complex environments. Recent advancements in deep learning technology, particularly Convolutional Neural Network (CNN), have introduced novel approaches and methodologies for the identification of radio signal modulation modes. This paper focuses on how deep learning techniques, particularly CNN, can improve the accuracy and efficiency of modulation mode recognition during satellite communications. It summarize key advances in dataset construction, network model design, training methods and optimization techniques. This paper also explores the potential application prospects of CNN technology in 6G communications, emphasizing its critical role in enhancing communication efficiency and service quality. The findings indicate that CNN has significant advantages in satellite communication modulation mode recognition, especially in improving recognition accuracy and robustness.

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The brain-computer interface has become a rapidly developing field, but it has also brought many problems with its development. The main issues are the sparse amount of brain-computer interface data, the inaccurate decoding and classification of data, and the data security of the brain-computer interface. With the development of artificial intelligence, artificial intelligence also provides solutions to many problems. This study mainly uses artificial intelligence algorithms to solve these problems. This paper reviews the integration of artificial intelligence techniques—specifically transfer learning, generative adversarial networks (GANs), Transformer models, and federated learning—to address critical challenges in brain-computer interfaces (BCIs), including data scarcity, classification accuracy, and data security. The hybrid model has many outstanding performances in solving the brain-computer interface problem, and this paper mainly mentions the joint extraction of spatiotemporal features of the CNN-Transformer to make up for the shortcomings of a single model and improve the overall performance. The GAN-TL hybrid model can effectively reduce the influence of individual differences on the model. This paper illustrates the advantages of the hybrid model, which is also the main direction of future research. It highlights how hybrid AI models significantly enhance BCI performance while outlining current limitations and future research directions to ensure robust, efficient, and secure BCI applications.

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Excavators, as specialized equipment for energy extraction and construction in engineering machinery, play an important role in the quality and effectiveness of construction. With the gradual introduction of the concept of green environmental protection into society, energy conservation and material reduction have become one of the main development directions of excavators. This article takes a small excavator as an example to study the optimization design of its boom from the perspective of mechanical structure optimization design. SolidWorks is used to create a three-dimensional model of the boom in the excavator's working device. Ansys Workbench is used to perform finite element static analysis on the boom, obtaining the stress situation and hazardous conditions under various working conditions. The optimized position is determined through the topology optimization module. Design a response surface optimization simulation test group using Design Expert, and generate an optimization regression equation for the boom based on the results. Under the conditions of satisfying stiffness and strength, the optimal solution for boom optimization was obtained through this equation. A comprehensive and feasible method and approach are proposed for the optimization design of excavator boom by modeling the optimal solution and ultimately analyzing and verifying its correctness.

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