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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28 March 2024
Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection
Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually...
27 March 2024
Detection of Oscillations in Process Control Loops from Visual Image Space Using Deep Convolutional Networks
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability. Although numerous automatic detection techniques have been proposed, most of them can only address part of the practical difficulties. An oscillation is heuristically defined as...
20 March 2024
Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information
This paper is concerned with consensus of a second-order linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network. A proportional-integral consensus protocol is designed by using delayed and memorized state information. Under the proportional-integral consensus protocol, the consensus problem of...
20 March 2024
A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends
When data privacy is imposed as a necessity, Federated learning (FL) emerges as a relevant artificial intelligence field for developing machine learning (ML) models in a distributed and decentralized environment. FL allows ML models to be trained on local devices without any need for centralized data transfer, thereby reducing both...
20 March 2024
A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface: Starlight High-Temperature Industrial Endoscope
Blast furnace (BF) burden surface contains the most abundant, intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control, optimize gas flow distribution and improve ironmaking efficiency. It has been challengeable to obtain high-quality optical burden surface images...
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12 February 2024
A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators with Global Fast Convergence
This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators. First, a fixed-time disturbance observer (FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems. Then an adaptive scheme is used...
12 February 2024
More Than Lightening: A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations
Low-light images suffer from low quality due to poor lighting conditions, noise pollution, and improper settings of cameras. To enhance low-light images, most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult. In contrast, a self-supervised method breaks free from the reliance...
12 February 2024
Dynamic Constraint-Driven Event-Triggered Control of Strict-Feedback Systems Without Max/Min Values on Irregular Constraints
This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints. Unlike the constraints considered in most existing papers, here the external irregular constraints are considered and a constraints switching mechanism (CSM) is introduced to circumvent the difficulties arising from irregular output constraints....
29 January 2024
Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects
This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure, control and decision-making, and perception and interaction, a holistic overview of the...
29 January 2024
Reinforcement Learning in Process Industries: Review and Perspective
This survey paper provides a review and perspective on intermediate and advanced reinforcement learning (RL) techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms, including fundamental concepts like Markov decision processes...

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