Modular and Distributed Supervisory Control Framework for Intelligent Micro-Manufacturing Systems with Unreliable Events

This paper presents a modular and distributed supervisory control integration framework for intelligent micro-manufacturing systems (MMSs) under event-level failures. Addressing the increasing demand for scalable and reliable supervisory control in both micro- and smart manufacturing, the proposed approach equips each subsystem with a detector automaton that classifies runtime states into Strictly robust, Recoverably robust, or Non-robust categories. Distributed supervisors then make real-time local decisions to ensure fault-tolerant evolution of system behaviors. Unlike conventional centralized or Petri net-based methods, the proposed automaton-based framework supports modular design and structural scalability. Quantitative comparisons show that the robustness-detection cost scales approximately linearly with the summed sizes of local graphs, indicating good structural scalability. Simulation studies validate the feasibility and scalability of the framework, demonstrating its effectiveness in maintaining production cycle reachability and its integration potential for micro-electro-mechanical systems (MEMS)-based production lines, micro-fabrication platforms, and smart factory environments. These results confirm that the proposed method can serve as a robust and deployable control layer for next-generation intelligent and micro-manufacturing integration architectures.