This paper investigates tracking control for the cooperative manipulation of dual-arm robots without velocity measurement, where prescribed performance specifications are imposed on the motion of the grasped object. An integrated dynamic model within the task space is formulated by establishing the interrelations that link the position and force constraints of the robotic arms to the grasped object. Subsequently, a motion model of the dual-arm system is derived, which remains unaffected by internal forces. To quickly obtain the object’s velocities with high precision, a practical prescribed-time velocity observer is constructed by incorporating a time-dependent scaling function. A revised performance boundary function is utilized to integrate the tracking errors associated with the grasping system, unlike existing approaches that define it solely as a time-dependent function. The barrier function is employed to map the complex output error constraints to new error variables, thereby facilitating the simplification of the controller design. Finally, simulation results are presented to illustrate and evaluate the effectiveness of the proposed method.

Dexterous manipulation of anthropomorphic multifinger robotic hands (MFRHs) is crucial for performing diverse and intricate tasks, where collaboration among the fingers is essential. This article presents a novel neural network-based composite learning strategy tailored for the synchronous control of multiple fingers in anthropomorphic MFRHs subjected to unknown dynamics and disturbances. By leveraging graph theory, the interconnections among fingers are delineated and integrated into the dynamic equations. The modified nonsingular terminal sliding mode (TSM) technique is employed to achieve fixed-time convergence of error variables without triggering singularity. Within the framework of composite learning, a novel computable prediction error is formulated by harnessing online historical data alongside the regression matrix. The combination of prediction errors and the regression matrix is utilized for parameter estimation, which, under a milder interval excitation (IE) condition, facilitates accurate parameter estimation without the requirement for the stringent persistent excitation (PE) condition. The feasibility and effectiveness of the proposed technique are demonstrated through simulation experiments.

Aspect-based sentiment analysis (ABSA) has attracted much attention due to its wide application scenarios. Most previous studies have focused solely on monolingual ABSA, posing a formidable challenge when extending ABSA applications to multilingual scenarios. In this paper, we study upgrading monolingual ABSA to cross-lingual ABSA. Existing methods usually exploit pre-trained cross-lingual language to model cross-lingual ABSA, and enhance the model with translation data. However, the low-resource languages might be under-represented during the pre-training phase, and the translation-enhanced methods heavily rely on the quality of the translation and label projection. Inspired by the observation that quantum entanglement can correlate multiple single systems, we map the monolingual expression to the quantum Hilbert space as a single quantum system, and then utilize quantum entanglement and quantum measurement to achieve cross-lingual ABSA. Specifically, we propose a novel quantum neural model named QPEN (short for quantum projection and quantum entanglement enhanced network). It is equipped with a proposed quantum projection module that projects aspects as quantum superposition on a complex-valued Hilbert space. Furthermore, a quantum entanglement module is proposed in QPEN to share language-specific features between different languages without transmission. We conducted simulation experiments on the classical computer, and experimental results on SemEval-2016 dataset demonstrate that our method achieves state-of-the-art performance in terms of F1-scores for five languages.