IEEE/CAA Journal of Automatica Sinica

An IEEE/CAA Journal of Automatica Sinica study proposes a new control method that enables autonomous agents to achieve a desired formation in a prescribed time, even in the presence of uncertain dynamics and persistent random disturbances.

An IEEE/CAA Journal of Automatica Sinica study presents M3Net, which combines reinforcement learning and meta-learning to improve hyperspectral image classification across unseen domains and to recognize unknown classes.

An IEEE/CAA Journal of Automatica Sinica study proposes CCDNN, a deep learning architecture for multi-source data fusion, with promising results in image reconstruction, fault diagnosis, and equipment lifetime prediction.

A recent IEEE/CAA Journal of Automatica Sinica study proposes a novel data-driven event-triggered control for linear systems with noisy data, helping minimize unnecessary controller updates.

A recent IEEE/CAA Journal of Automatica Sinica survey comprehensively reviews rough feature selection, a powerful technique for tackling high-dimensional data using rough set theory.

An IEEE/CAA Journal of Automatica Sinica review examines more than 100 datasets, evaluation metrics, and benchmarks used in autonomous driving research, highlighting challenges in standardization and directions for future studies.

A recent study published in IEEE/CAA Journal of Automatica Sinica proposes PDPGNN, a personalized approach to allocating privacy-protecting noise during graph neural network training, improving accuracy while preserving privacy.

IEEE/CAA Journal of Automatica Sinica study proposes a data-driven predictive control framework for unknown nonlinear systems with intermittent observations, using reinforcement learning and framing the problem as a nonzero-sum game.

An IEEE/CAA Journal of Automatica Sinica study proposes a predictive adaptive controller for accurate and efficient 3D trajectory tracking of autonomous underwater vehicles under ocean currents and model uncertainty.

A new IEEE/CAA Journal of Automatica Sinica paper successfully demonstrates automatic OPC UA information modeling from textual data using a large language model.

To represent a dynamic graph precisely, a new IEEE/CAA Journal of Automatica Sinica study introduces a graph tensor alliance attention network that outperforms existing techniques.

A new study investigates fuzzy prescribed-time control with fast convergence for uncertain nonlinear pure feedback systems.

A recent study published in the IEEE/CAA Journal of Automatica Sinica proposes a convex optimization-based model predictive control strategy for Mars ascent vehicle guidance system.

In a new IEEE/CAA Journal of Automatica Sinica breakthrough study, a team of researchers achieves open-vocabulary 3D scene segmentation via dual-modal interaction.

An IEEE/CAA Journal of Automatica Sinica study proposes a data-driven iterative learning consensus tracking approach based on robust neural models for unknown heterogeneous nonlinear multiagent systems with input constraints. Read here: ieee-jas.net/en/article/doi/…

A study published in IEEE/CAA Journal of Automatica Sinica presents an algebraic approach to stochastic optimal control, which shows that solutions to the stochastic Riccati equation can inherit the algebraic structure of system matrices. ieee-jas.com/en/article/doi/…

A recent IEEE/CAA Journal of Automatica Sinica article introduces an advanced model predictive control approach for human-robot interaction in exoskeleton robots powered by series elastic actuators, delivering adaptive support and reliable performance.

To handle distributed resource allocation in multi-coalition games, where conflicts of interest are prominent, an IEEE/CAA Journal of Automatica Sinica study introduces a new model called the intra-independent resource allocation game.