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

Area of Science:

• Manufacturing Engineering
• Control Systems Engineering
• Computer Science

Background:

• Increasing demand for scalable and reliable supervisory control in micro- and smart manufacturing.
• Limitations of conventional centralized or Petri net-based control methods for complex systems.

Purpose of the Study:

• To present a modular and distributed supervisory control integration framework for intelligent micro-manufacturing systems (MMSs) capable of handling event-level failures.
• To ensure fault-tolerant evolution of system behaviors through real-time local decisions.
• To provide a scalable and robust control layer for next-generation manufacturing architectures.

Main Methods:

• Development of a detector automaton for each subsystem to classify runtime states (Strictly robust, Recoverably robust, Non-robust).
• Implementation of distributed supervisors for real-time local decision-making.
• Quantitative comparisons and simulation studies to validate the framework's performance and scalability.

Main Results:

• The proposed automaton-based framework supports modular design and structural scalability.
• Robustness-detection cost scales approximately linearly with the summed sizes of local graphs, indicating good structural scalability.
• Simulation studies validate the framework's feasibility, scalability, and effectiveness in maintaining production cycle reachability.

Conclusions:

• The developed framework offers a robust and deployable control layer for intelligent micro-manufacturing integration.
• It demonstrates significant integration potential for micro-electro-mechanical systems (MEMS)-based production lines, micro-fabrication platforms, and smart factory environments.
• The approach overcomes limitations of traditional methods by enabling modularity and distributed decision-making for enhanced fault tolerance.