Guest Editor(s)
Prof. Dr. Nguyen Truong Thinh
Email: thinhnt@ueh.edu.vn
Affiliation: College of Technology and Design, University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam
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Research Interests: intelligent automation, soft computing, robotics and mechatronics, advanced control systems, artificial intelligence applications, decision-support systems, biomedical engineering, smart energy systems, cyber-physical systems
Summary
The rapid development of artificial intelligence, soft computing, sensing technologies, and intelligent automation is transforming modern cyber-physical systems in smart agriculture, industrial control, decision-making, and sustainable energy. These systems operate in dynamic, uncertain, and data-rich environments where conventional modeling and control methods may be insufficient. Soft computing methods, including fuzzy systems, neural networks, evolutionary computation, swarm intelligence, reinforcement learning, and hybrid intelligent algorithms, offer flexible and adaptive solutions for uncertainty, nonlinearity, incomplete information, optimization, and real-time decision-making.
This Special Issue aims to provide a multidisciplinary forum for researchers and practitioners to present recent advances in soft computing-driven intelligent automation for adaptive cyber-physical systems. Suggested themes include intelligent control, adaptive and robust control, observer-based control, AI-assisted diagnosis and monitoring, smart agriculture, decision-support systems, smart manufacturing, sustainable energy, smart grids, industrial automation, digital twins, edge intelligence, IoT-enabled systems, predictive maintenance, and explainable AI. Contributions with methodological novelty, experimental validation, interpretability, robustness, and practical engineering impact are especially welcome.
The objectives of this Special Issue are:
- To advance soft computing-driven intelligent automation methods for adaptive cyber-physical systems operating under uncertainty, nonlinear dynamics, and changing environments.
- To promote novel computational approaches, including fuzzy systems, neural networks, evolutionary computation, reinforcement learning, and hybrid intelligent models for modeling, optimization, control, prediction, and decision-making.
- To highlight robust, adaptive, interpretable, and real-time solutions for complex systems in smart agriculture, industrial automation, robotics, sustainable energy, and intelligent infrastructure.
- To encourage the integration of soft computing, sensing technologies, IoT, digital twins, and advanced control strategies for intelligent and autonomous system development.
- To foster interdisciplinary research with strong theoretical contributions, experimental validation, and practical engineering impact in adaptive cyber-physical systems.
Keywords
soft computing, intelligent automation, cyber-physical systems, artificial intelligence, intelligent control, smart agriculture, sustainable energy systems, decision support systems, digital twins
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