Scope
The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
IEEE Transactions on Cybernetics replaced the IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics on January 1, 2013.
Editor-in-Chief
Peng Shi
Editor-In-Chief
School of Electrical and Electronic Engineering,
The University of Adelaide, Australia
Articles
03 September 2026
This article explores an event-triggered self-learning parallel tracking control strategy for continuous-time nonlinear systems with actuator faults. To mitigate the impact of actuator faults, an enhanced performance index function is developed based on the unknown upper bound of such faults. Furthermore, an augmented error nonlinear system is constructed by incorporating...
02 September 2026
Safe trajectory tracking for nonlinear stochastic systems operating in obstacle-cluttered environments remains a significant challenge, as random disturbances and obstacle-induced constraints can simultaneously degrade tracking accuracy and threaten system safety. To overcome this issue, this article develops a safety-aware optimal tracking control framework that integrates stochastic control barrier functions (CBFs)...
24 August 2026
Value alignment plays a crucial role in human–artificial intelligence (AI) collaboration. Traditional approaches attempt to infer human goals from actions to guide AI policies. However, this behavior-level alignment faces an inherent challenge: the ambiguous mapping between goals and actions, as a single action might serve multiple possible goals, while different...
17 August 2026
This article develops a practical fixed-time tracking control framework for a class of strict-feedback nonlinear systems subject to unknown external disturbances. A fixed-time disturbance observer (FxTDO) is first designed to reconstruct the disturbances together with their higher-order derivatives within a uniform settling time whose upper bound is independent of the...
14 August 2026
As deep learning (DL) performs remarkably in pattern recognition from complex data, it is used to interpret user intentions from electroencephalography (EEG) signals. However, the DL models trained on EEG datasets have low generalization ability owing to numerous noisy samples in datasets. Therefore, prior research has focused on distinguishing and...
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