Given that grinding robots are easily affected by internal and external disturbances when machining complex surfaces with high precision, in this study, an adaptive robust impedance control method combining a radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. In a Cartesian coordinate system, we first use the universal approximation ability of the RBFNN to accurately identify and actively compensate for complex unknown disturbances in robot dynamics online. Then, an improved sliding mode impedance controller, which uses robust sliding mode control to effectively suppress the influence of RBFNN identification error and residual disturbance on trajectory tracking and ensure the accuracy of impedance control, is implemented. This approach improves the control performance and overcomes the inherent chattering phenomenon of the traditional sliding mode.

An image–text retrieval method that integrates intramodal fine-grained local semantic information and intermodal global semantic information is proposed to address the weak fine-grained discrimination capabilities for the semantic features located between image and text modalities in cross-modal retrieval tasks. First, the original features of images and texts are extracted, and a graph attention network is employed for region relationship reasoning to obtain relation-enhanced local features. Then, an attention mechanism is used for different semantically interacting samples within the same modality, enabling comprehensive intramodal relationship learning and producing semantically enhanced image and text embeddings. Finally, a triplet loss function is used to train the entire model, and it is enhanced with an angular constraint. Through extensive comparative experiments conducted on the Flickr30K and MS-COCO benchmark datasets, the effectiveness and superiority of the proposed method were verified. It outperformed the current method by 6.4% relatively for image retrieval and 1.3% relatively for caption retrieval on MS-COCO (Recall@1 using the 1K test set).

This paper proposes a generalized high-order linear active disturbance rejection control (GHO-LADRC) method to suppress non-stationary disturbances in VICTS antenna direct-drive motors during high-dynamic scanning. First, a fourth-order generalized extended state observer is constructed, in which the derivative of the total disturbance is explicitly modeled as an extended state. This configuration enables real-time observation of the disturbance rate of change and suppresses the phase lag inherent in traditional ADRC during rapid disturbance variations through disturbance feedforward compensation. Secondly, drawing on singular perturbation theory and the motor’s dual-time-scale characteristics, this work precisely decouples and explicitly extracts the nonlinear friction and electromagnetic damping terms during the modeling stage. By integrating the extracted electromagnetic damping terms and the disturbance variation rate, an improved model-assisted control law is formulated, enabling active compensation for intense dynamic interference. Theoretical analysis and experimental results demonstrate that the proposed method significantly enhances disturbance rejection capability and satellite communication accuracy. As the first application of GHO-LADRC in the field of direct-drive VICTS antenna control, this work validates its effectiveness in improving system robustness within complex dynamic environments.