Petri Net controller synthesis based on decomposed manufacturing models

Area of Science:

• Control Theory
• Computer Science
• Discrete Event Systems

Background:

• Supervisory control theory application to large-scale systems using Petri Nets (PN) faces challenges due to state space explosion and uncontrollable events.
• Uncontrollable events can lead to forbidden states, necessitating linear constraints for system control.
• Existing constraint reduction methods struggle with the complexity of large-scale systems.

Purpose of the Study:

• To propose a novel method for controller synthesis specifically designed for large-scale systems modeled with Petri Nets.
• To address the computational challenges associated with enforcing constraints in complex PN models.
• To facilitate efficient and effective controller design for industrial applications.

Main Methods:

• Decomposition of the original large Petri Net model into smaller, manageable sub-models.
• Application of supervisory control principles to these smaller PN models.
• Development of techniques to reduce and enforce linear constraints within the decomposed framework.

Main Results:

• Significant reduction in computational cost compared to traditional methods.
• Demonstrated feasibility and effectiveness of constraint enforcement on decomposed PN models.
• Successful controller synthesis for large-scale systems validated through appropriate results.

Conclusions:

• The proposed method offers a computationally efficient approach to controller synthesis for large-scale systems using Petri Nets.
• Decomposition of PN models is a viable strategy to overcome the limitations of existing methods.
• This technique provides a valuable tool for designing controllers in complex industrial automation and control scenarios.