Journal of Artificial Intelligence & Control Systems (JAICS) is a peer-reviewed, open-access journal dedicated to the intersection of artificial intelligence (AI) and control systems. JAICS is committed to publishing high-quality research articles that advance the understanding and application of AI in control systems across various disciplines. The mission of JAICS is to serve as a premier platform for scholars, researchers, and practitioners to share cutting-edge research, innovative methodologies, and practical applications that integrate AI with control systems. We aim to foster a collaborative environment that promotes interdisciplinary dialogue and accelerates the development of intelligent control solutions.

JAICS invites submissions that span a wide range of topics within the realm of AI and control systems. Our scope includes, but is not limited to the following areas:

- AI in Control Systems: research on the application of AI techniques such as machine learning, deep learning, fuzzy logic, and evolutionary algorithms in the design and optimization of control systems.

- Adaptive and Intelligent Control: studies on adaptive control, intelligent control, and autonomous systems that utilize AI to enhance performance and robustness.

- Control Theory and AI: theoretical advancements in control theory that incorporate AI principles, including nonlinear control, optimal control, and predictive control.

- Robotics and Automation: research on AI-driven control systems for robotics and automation, including motion control, sensor fusion, and autonomous navigation.

- Industrial Applications: practical implementations of AI in industrial control systems, such as process control, manufacturing automation, and smart grids.

- Cyber-Physical Systems: integration of AI in cyber-physical systems for real-time control, security, and resilience.

- Human-Machine Interaction: studies on the interaction between humans and AI-controlled systems, including cooperative control and human-in-the-loop systems.

- Data-Driven Control: research on data-driven control methods that leverage AI for system identification, prediction, and decision-making.

- Ethics and Societal Impact: examination of the ethical considerations and societal implications of AI in control systems, including privacy, security, and fairness.

- Education and Outreach: innovative approaches to teaching AI and control systems, as well as efforts to engage the public in understanding the role of AI in control systems.