Distributed Autonomous Control Systems (DACs), the core infrastructure of modern industrial production, intelligent transportation and emergency response, face dual risks of data privacy leakage and malicious attacks due to their open communication and distributed collaboration. Traditional separate protection schemes cause resource conflicts and performance trade-offs, failing to meet high-reliability demands in safety-critical scenarios. This paper... more

Corresponding Author: Li Wei; Email: Abstract: Autonomous control systems are increasingly integrated into industrial production, smart cities, and robotics, demanding higher adaptability to complex and dynamic environments. This study explores the application of artificial intelligence (AI) technologies, including deep reinforcement learning and fuzzy logic, in adaptive optimization of autonomous control systems. It analyzes recent advances,... more

Cyber-Physical Systems (CPS) are evolving towards higher autonomy and real-time responsiveness, posing stringent demands on low-latency decision-making and dynamic environmental adaptation. Traditional cloud-centric control architectures suffer from inevitable network delays and bandwidth bottlenecks, limiting their applicability in time-critical scenarios. This paper proposes a novel synergistic framework integrating Edge Intelligence (EI) and Digital Twin (DT) for... more

Distributed Autonomous Control Systems (DACs) are widely used in safety-critical fields like intelligent transportation and industrial automation, yet face growing threats from Byzantine attacks, data poisoning and jamming that may cause catastrophic failures. Federated learning (FL) addresses DACs’ privacy and communication issues but lacks dedicated security mechanisms for its training and deployment phases. This paper... more