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This paper studies the practical prescribed-time control problem for dual-arm robots handling an object with output constraints. Firstly, by utilizing the property that the sum of internal forces in the grasping space is zero, the system model is obtained and decomposed into the contact force model and free motion model, which are orthogonal to each other. Furthermore, by combining the performance function and constraint function, the original system tracking error is transformed to a new one, whose boundedness can ensure that the original system variable converges to the predetermined range within the specified time. Then, a comprehensive neuroadaptive controller including position control term and contact control force control term is designed. Finally, the simulation results of two planar three link robots working together on a common object verify the effectiveness and superiority.

A prescribed-performance-based sliding mode control method with feed-forward inverse compensation is proposed in this study to improve the micropositioning accuracy and convergence speed of a piezoelectric actuator (PEA). Firstly, the piezo-actuated micropositioning system is described by a Hammerstein structure model, and an inverse Prandtl–Ishlinskii (PI) model was employed to compensate for its hysteresis characteristics. Then, considering modelling errors, inverse compensation errors, and external disturbances, a new prescribed performance function (PPF) with an exponential dynamic decay rate was developed to describe the constrained region of the errors. We then transformed the error into an unconstrained form by constructing a monotonic function, and the sliding variables were obtained by using the transformation error. Based on this, a sliding mode controller with a prescribed performance function (SMC-PPF) was designed to improve the control accuracy of PEAs. Furthermore, we demonstrated that the error can converge to the constrained region and the sliding variables are stable within the switching band. Finally, experiments were conducted to verify the speed and accuracy of the controller. The step-response experiment results indicated that the time taken for SMC-PPC to enter the error window was 8.1 and 2.2 ms faster than that of sliding mode control (SMC) and PID, respectively. The ability of SMC-PPF to improve accuracy was verified using four different reference inputs. These results showed that, for these different inputs, the root mean square error of the SMC-PPF was reduced by over 39.6% and 52.5%, compared with the SMC and PID, respectively.

To solve the problem of nonlinear characteristics neglecting and fault mechanism analysis lacking in fault diagnosis research, a new method of fault mechanism analysis and diagnosis based on nonlinear spectrum is proposed. Firstly, based on the Permanent Magnet Synchronous Motor (PMSM) model of robot, the first 4-order spectrums based on nonlinear output frequency response function (NOFRF) in different states are obtained by batch calculation method. Secondly, the high-frequency spectrum distribution rule of NOFRF spectrum in different states are analyzed. Finally, in the closed-loop simulation environment of robot, the identification method based on data-driven is adopted for NOFRF spectrum calculation to verify power loss fault of PMSM. Meanwhile, the fault diagnosis experiment is also carried out. The experimental results indicate that the key characteristics distribution rule of NOFRF spectrums in the real environment is consistent with the theoretical analysis results, and compared with the traditional fault feature extraction methods by output signal, the diagnosis with fault feature of NOFRF spectrum for industrial robot closed-loop drive system has the highest accuracy, which verifies the validity of NOFRF spectrum as the fault feature.

According to the mirror mechanism the discharge of F5 mirror neurons of a monkey observing another individual performing an action is a motor representation of the observed action that may serve to understand or learn from the action. This hypothesis, if strictly interpreted, requires mirror neurons to exhibit an action tuning that is shared between action observation and execution. Due to insufficient data it remains contentious if this requirement is met. To fill in the gaps, we conducted an experiment in which identical objects had to be manipulated in three different ways in order to serve distinct action goals. Using three methods, including cross-task classification, we found that at most time points F5 mirror neurons did not encode observed actions with the same code underlying action execution. However, in about 20% of neurons there were time periods with a shared code. These time periods formed a distinct cluster and cannot be considered a product of chance. Population classification yielded non-shared coding for observed actions in the whole population, which was at times optimal and consistently better than shared coding in differentially selected subpopulations. These results support the hypothesis of a representation of observed actions based on a strictly defined mirror mechanism only for small subsets of neurons and only under the assumption of time-resolved readout. Considering alternative concepts and recent findings, we propose that during observation mirror neurons represent the process of a goal pursuit from the observer’s viewpoint. Whether the observer’s goal pursuit, in which the other’s action goal becomes the observer’s action goal, or the other’s goal pursuit is represented remains to be clarified. In any case, it may allow the observer to use expectations associated with a goal pursuit to directly intervene in or learn from another’s action.

As a critical component of rail travel, the train communication network (TCN) is an integrated central platform that is used to realize the train control, condition monitoring, and data transmission, whose failure will disrupt the symmetry of TCN topology and endanger the security of rail trains. To enhance the reliability of TCN, an intelligent fault diagnosis method is proposed based on active learning (AL) and a stacked consistent autoencoder (SCAE), which is capable of building a competitive classifier with a limited amount of labeled training samples. SCAE can learn better feature presentations from electrical multifunction vehicle bus (MVB) signals by reconstructing the same raw input data layer by layer in the unsupervised feature learning phase. In the supervised fine-tuning phase, a deep AL-based fault diagnosis framework is proposed, and a dynamic fusion AL method is presented. The most valuable unlabeled samples are selected for labeling and training by considering uncertainty and similarity simultaneously, and the fusion weight is dynamically adjusted at the different training stages. A TCN experimental platform is constructed, and experimental results show that the proposed method achieves better performance under three different metrics with fewer labeled samples compared to the state-of-the-art methods; it is also symmetrically valid in class-imbalanced data.

In data-centric industries, such as finance, healthcare, and cybersecurity, maintaining the integrity and accuracy of data lineage is crucial due to compliance requirements. Current methods for verifying data lineage often struggle with the dynamic and multi-sourced nature of datasets, as well as their scale, resulting in reduced performance in detecting anomalies or validating lineage. In this article, we introduce and provide an empirical assessment of two artificial intelligence-based frameworks for data lineage verification and anomaly detection, which complement each other. In the first phase, we developed an unsupervised approach using Graph Attention Networks (GATs) for structural representation learning and an Isolation Forest for ‘no-label’ anomaly detection. The model surrogate for validation produced over 99.8% accurate reporting on replicated anomaly patterns in unseen test data for Olist, Transaction Processing Council Benchmark H (TPC-H), and Medical Information Mart for Intensive Care III (MIMIC-III). In the second phase, we developed a supervised, multi-modal framework that integrates graph neural networks (GNNs), long short-term memory (LSTM)-Attention networks, Dynamic Time Warping (DTW) for automatic labeling, and Contrastive Learning. To counter the integrated class imbalance of the anomaly detection class, this framework incorporates the Synthetic Minority Over-Sampling Technique (SMOTE) as a fundamental component of its training. Comparing both models on three datasets, the unsupervised model outperforms the supervised model due to its ability to dynamically adapt to data without requiring labels. The supervised model achieves a maximum area under the curve (AUC) of 0.96, and the unsupervised model achieves an AUC of 1.00, indicating better prediction efficiency. The multi-faceted comparison of performance, feature importance, and operational dashboards provides the user with valuable insights, thereby confirming the effectiveness of the first unsupervised model and the second supervised multi-modal model, while fully retaining the explainability, governance, and scalability of data lineage in a comparative pair.

Dual humanoid robot collaborative control systems possess better flexibility and adaptability in complex environments due to their similar structures to humans. This paper adopts a distributed model predictive controller based on the leader–follower approach to address the collaborative transportation control issue of dual humanoid robots. In the dual-robot collaborative control system, network latency issues may arise due to unstable network conditions, affecting the consistency of dual-robot collaboration. To solve this issue, a communication protocol was constructed through socket communication for dual-robot collaborative consistency, thereby resolving the problem of consistency in dual humanoid robot collaboration. Additionally, due to the complex structure of humanoid robots, there are deficiencies in position tracking accuracy during movement. To address the poor accuracy in position tracking, this paper proposes a distributed model predictive control that considers historical cumulative error, thus enhancing the position tracking accuracy of dual-robot collaborative control.

Background Since its discovery in the late 20th century, research on mirror neurons has become a pivotal area in neuroscience, linked to various cognitive and social functions. This bibliometric analysis explores the research trajectory, key research topics, and future trends in the field of mirror neuron research. Methods We searched the Web of Science Core Collection (WoSCC) database for publications from 1996 to 2024 on mirror neuron research. Statistical and visualization analyses were performed using CiteSpace and VOSviewer. Results Publication output on mirror neurons peaked in 2013 and remained active. High‐impact journals such as Science, Brain, Neuron, PNAS, and NeuroImage frequently feature findings on the mirror neuron system, including its distribution, neural coding, and roles in intention understanding, affective empathy, motor learning, autism, and neurological disorders. Keyword clustering reveals major directions in cognitive neuroscience, motor neuroscience, and neurostimulation, whereas burst detection underscores the emerging significance of brain‐computer interfaces (BCIs). Research methodologies have been evolving from traditional electrophysiological recordings to advanced techniques such as functional magnetic resonance imaging, transcranial magnetic stimulation, and BCIs, highlighting a dynamic, multidisciplinary progression. Conclusions This study identifies key areas associated with mirror neurons and anticipates that future work will integrate findings with artificial intelligence, clinical interventions, and novel neuroimaging techniques, providing new perspectives on complex socio‐cognitive issues and their applications in both basic science and clinical practice. We conducted a bibliometric analysis of mirror neuron research to examine global publication trends, methodological advancements, and emerging applications. Our findings highlight robust international collaborations, advanced imaging, neurotechnological and immersive technologies, and the translational potential of mirror neuron studies for understanding social cognition and enhancing clinical interventions.

The NAO robot integrates sensors, vision systems, and control systems. Its monocular vision system is adopted to locate the target object in the three-dimensional space of robots. Firstly, a positioning model based on monocular vision is established according to the principle of small hole perspective. Then, the position coordinates of the target center are obtained in the image coordinate system. In the model mentioned above, the relationship between position coordinates and image coordinates is established at a certain space height. According to this relationship, the two-dimensional coordinates in the image are converted into the three-dimensional coordinates in the robot coordinate system. After getting the target location, we establish the navigation map and find the optimal path under the unknown environment. Based on the simultaneous localization and the mapping (SLAM) theory, the sonar sensor of the NAO robot is used to detect the distance between the robot and the obstacles or between the robot and the end landmark. Moreover, the sonar sensor and the camera are used to distinguish the obstacle and the landmark. After the navigation map is built, the bi-directional parallel search strategy and the simulated annealing algorithm are introduced to improve the traditional artificial bee colony algorithm, and the improved artificial bee colony algorithm is proposed to find an optimal path in the navigation map. Finally, the experimental results show that based on the built environment map, the robot can find an optimal path from the origin to the landmark on the premise of avoiding obstacles.

Low switching frequency is usually used in high-power wind grid-tied inverter systems to reduce power loss caused by on–off switching activity. Proportional integral multi-resonant type repetitive control (PIMR-type RC) can track reference grid current signals and suppress harmonic signals of grid-tied inverter system. A low switching frequency will result in a low sampling rate of control system. However, integer-order phase-lead compensation will lead to a poor compensation accuracy of PIMR-type RC with a low sampling rate, leading to poor tracking and suppressing performance of PIMR-type RC, and even result in system instability. To solve these problems, a high-performance fractional-order phase-lead compensation PIMR-type RC (FO-PIMR-RC) scheme is proposed in this paper. Fractional-order phase-lead compensation is adopted to compensate accurately the phase lag caused by controlled plant and PIMR-type RC and approximately realized by a finite impulse response (FIR) filter. Stability analysis and harmonic suppression performance are provided, and the parameter optimization design is implemented. Simulation and experimental results prove the desirable performance of the proposed control scheme.