Intelligent Control Systems aims to publish original, innovative, and influential research in intelligent control theory, methodologies, and systems, promoting the deep integration of control science, artificial intelligence, and system engineering. The journal particularly welcomes contributions that advance intelligent decision-making, learning-based mechanisms, optimization algorithms, and real-time autonomous control, with applications spanning cyber-physical, industrial, biological, and socio-technical systems.
A core focus lies in novel theories, algorithms, and system architectures that exhibit self-learning, adaptability, robustness, and autonomy in uncertain, nonlinear, and data-rich environments, thereby supporting the evolution of next-generation intelligent and autonomous systems.
Topics of interest include, but are not limited to:
- Intelligent, adaptive, and learning-based control
- Machine learning and AI-enabled control systems
- Reinforcement learning and data-driven control
- Neural, fuzzy, evolutionary, and hybrid intelligent control
- Optimal, robust, nonlinear, and model predictive control
- Networked, distributed, and multi-agent control systems
- Fault-tolerant, resilient, and secure control
- Real-time control, embedded systems, and edge intelligence
- Robotics, unmanned systems, and human--machine interaction
- Intelligent control for smart manufacturing and Industry 4.0
- Smart grids, renewable energy systems, and intelligent energy management
- Intelligent transportation and mobility systems
- Biomedical, healthcare, and biological control systems
- Cyber-physical systems and digital twins
- Safe, verifiable, and explainable AI for autonomous systems
- Quantum computing and optimization for intelligent control
Article Types
The journal accepts submissions in the following categories:
- Original Research Articles
- Reviews and Perspectives
- Methods and Protocols
- Theoretical and Computational Studies
- Communication and Short Reports
- Application Notes and Technology Spotlights
