We are thrilled to announce the launch of the Journal of Artificial Intelligence & Control Systems (JAICS), a peer-reviewed, open-access journal dedicated to advancing research at the intersection of artificial intelligence (AI) and control systems. As AI techniques such as deep learning and reinforcement learning evolve rapidly, and control system requirements become increasingly complex, JAICS emerges to meet the need for a specialized platform in this intersection.

AI and control systems are at the forefront of modern technological advancements, driving innovation across industries such as manufacturing, robotics, transportation, and healthcare. The integration of AI into control systems has transformative effects. In manufacturing, AI-powered control optimizes production processes, minimizes waste, and enhances efficiency. Predictive maintenance systems, for example, leverage AI to anticipate equipment failures, reducing downtime and operational costs. In robotics, AI-driven control enables precision tasks in critical applications such as surgery and hazardous environments. Autonomous vehicles rely on AI for real-time decision-making while control systems ensure safety and stability.

Despite its potential, AI-driven control faces significant challenges, including handling high-dimensional and uncertain data, ensuring system reliability, and addressing security concerns. Addressing these issues requires collaboration across multiple disciplines, including computer science, electrical engineering, and mathematics. JAICS aims to foster this interdisciplinary dialogue and accelerate the development of intelligent control solutions.

JAICS will highlight key scientific and technological advancements in AI-driven control systems, including (but not limited to):

Fundamental Theories of AI -- Machine learning, deep learning, reinforcement learning, knowledge representation, reasoning, natural language processing, and computer vision.

AI in Control Systems -- Applications of AI techniques such as machine learning, deep learning, fuzzy logic, and evolutionary algorithms in control system design and optimization.

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

Control Theory and AI -- Theoretical advancements integrating AI with nonlinear control, optimal control, and predictive control.

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

Industrial Applications -- AI applications in industrial control systems, including process control, manufacturing automation, and smart grids.

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

Human-Machine Interaction -- Research on cooperative control, human-in-the-loop systems, and intuitive AI-driven interfaces.

Data-Driven Control -- AI-based approaches for system identification, prediction, and decision-making in control.Education and Outreach -- Innovative methods for teaching AI and control systems and increasing public engagement in this field.

JAICS welcomes original research articles, short communications, review articles, and case studies:

Original research papers should present novel theoretical contributions or innovative applications.

Short communications report preliminary findings with the high potential impact.

Review articles provide comprehensive insights into emerging research trends.Case studies provide practical perspectives on real-world applications.

We will extend our gratitude to the authors who contribute to our inaugural issue, as well as to the editorial board, reviewers, and production team for their invaluable support. With collective effort, JAICS will serve as a premier resource for researchers worldwide. We invite scholars from academia and industry to contribute, fostering innovation at the frontier of AI and control systems.

Together, let's unlock the full potential of AI-driven control solutions!

All authors contributed equally, read and agreed to the published version of the manuscript.

We should sincerely thank Yunda Yan for  his work in article polish and improvement.

The authors declare no conflicts of interest.