IEEE SMC: Systems, Man, & Cybernetics Society

IEEE/CAA Journal of Automatica Sinica

Scope

The scope of the IEEE/CAA Journal of Automatica Sinica includes the field of automation. The objective of this journal is high quality and rapid publication of articles, with a strong focus on new trends, original theoretical and experimental research and developments, emerging technologies, and industrial standards in automation. Specifically, the Journal focuses on such areas as automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network based automation, robotics, computer-aided technologies for automation systems, sensing and measurement, navigation, guidance, and control, smart city, smart grid, big data and data mining, internet of things, cyber-physical systems, blockchain, cloud computing for automation, mechatronics.

IEEE/CAA Journal of Automatica Sinica was lunched on January 1, 2014.

Editor-in-Chief

Qing-Long Han
Editor-In-Chief

Distinguished Professor, PhD, MAE, FIEEE, FIFAC, FIEAust
Member of the Academia Europaea (The Academy of Europe)

Pro Vice-Chancellor (Research Quality)

Swinburne University of Technology
EN Building, Level 6, Room 602c
John Street, Hawthorn, Melbourne, Victoria 3122, Australia

Tel.:  +61 3 9214 3808

Fax.:  +61 9214 8264

Email: [email protected]

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Articles

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01 October 2026
Output Consensus for Heterogeneous Two-Time-Scale Multiagent Systems Under DoS Attacks: A Multi-Index Case
This article studies the output consensus issue for a class of heterogeneous two-time-scale multiagent systems (HTTS-MASs) under denial-of-service (DoS) attacks. A novel output consensus control framework, integrating a resilient distributed observer and a composite controller, is built as the first attempt for HTTSMASs. By establishing a DoS attack impact interval...
01 October 2026
Damping-Enhanced Zero-Effort-Miss Guidance for 3D Engagement: Strengthened Parallel Approach with Maneuvering Target Robustness
Dear Editor, This letter proposes a novel three-dimensional (3D) guidance law based on the zero-effort-miss (ZEM) concept enhanced with a damping term. The key concept involved in the approach is the introduction of a damping term into the guidance law which effectively increases the degrees of freedom and strengthens the...
01 October 2026
Synchronization of Two-Layer Multi-Weighted Networks by Finite-Time Privacy Protection
Recently, privacy breaches caused by information exchange between networks have received increasing attention. This paper investigates finite-time synchronization (FntS) of a two-layer multi-weight network using privacy preserving (PP) methods. Initially, a novel finite-time (FnT) time-varying vanishing privacy mask function is developed. Secondly, FntS criterion for multi-weight network based on PP...
01 October 2026
Reinforcement Learning-Based Predefined-Time Optimal Bipartite Consensus of High-Order Multiagent Systems
Dear Editor, This letter addresses the predefined-time bipartite consensus issue for high-order nonlinear multiagent systems. Firstly, a novel class of fuzzy logic systems-based predefined-time control method is proposed by using reinforcement learning technique and backstepping control method. The systems' nonlinear dynamics and control behaviors can be trained effectively through fuzzy...
01 October 2026
A New Parameter Estimation Methodology Using Steady State Yaw Rate Measurements for Lateral Vehicle Dynamics
In this paper, the lateral dynamics of road vehicles (LDRV) is further studied from the viewpoint of vehicle informatics. It is seen that LDRV is first decoupled and the vehicle slip angle is proved to be observable from the yaw rate measurements. A new methodology of parameter estimation using steady-state...
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28 August 2026
Nonlinear Multiagent Systems-Oriented Privacy Protection and Convergence Predefinition
This paper focuses on the convergence-predefined problem for nonlinear multiagent systems with a privacy-preserving mechanism. Herein, a privacy-preserving mechanism based on the binary vector with the encryption-decryption algorithm is proposed and imposed on the system outputs to guarantee the security of the signal transmission, while realizing the pre-defmed-time convergence of...
28 August 2026
Fault-Tolerant Formation Control of Multi-UAVs Using Prescribed Funnel and Relative Position Measurement
This paper studies a fault-tolerant formation control (FTFC) scheme for multiple unmanned aerial vehicles (multi-UAVs) without inter-agent communication under input saturation, actuator faults, and disturbances. Firstly, according to the obtained relative position measurement in the absence of inter-agent communication, a fixed-time differentiator is designed to achieve the estimate of the...
28 August 2026
A Distributed Pre-Defined-Time Solution to Time-Varying Constrained Optimization Problems Over Multiplex Networks
Numerous engineering applications involve optimization with constrained conditions. This study proposes a novel distributed algorithm for solving time-varying (TV) constrained optimization problems over multiplex networks within the predefined time, where both the objective functions and constraints are TV. By integrating a time-regulator function and the supra-Laplacian matrix, we develop an...
28 August 2026
Brain-Inspired GNSS Spoofing Attack Cognition Using Spiking Neural Network for Train Positioning
Global Navigation Satellite System (GNSS) has been an exciting growing research field in railway transportation due to its accuracy, low cost, and global availability. However, GNSS-based train positioning is susceptible to spoofing attacks in complex railway operation environments due to GNSS vulnerabilities. Aiming at resilience assurance of train positioning using...
28 August 2026
Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation, and Future Trends
Remaining Useful Life (RUL) prediction for turbofan engines plays a vital role in predictive maintenance, ensuring operational safety and efficiency in aviation. Although data-driven approaches using machine learning and deep learning have shown potential, they face challenges such as limited data and distribution shifts caused by varying operating conditions. Domain...

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