Study of workshop network stability based on pinning control in disturbance environment

Area of Science:

• Industrial Engineering
• Control Systems
• Network Science

Background:

• Production processes are susceptible to complex dynamic fluctuations from disturbance factors.
• Ensuring stability in manufacturing environments under constraints presents a significant challenge.

Purpose of the Study:

• To propose an improved coupled map lattice workshop production network state model.
• To design a resource load protection controller and a pinning control-based network state model.
• To develop and evaluate three stability control strategies: Self-adaption Control (SAC), Self-acting Control (SC), and Pinning Control (PC).

Main Methods:

• Development of an improved coupled map lattice model for workshop production networks.
• Design of a controller incorporating resource load protection.
• Implementation of three distinct stability control strategies (SAC, SC, PC) based on disturbance triggering and node state transitions.
• Introduction of Recovery Time Steps (RTS) and Node Failure Times (NFT) as control effect evaluation indexes.

Main Results:

• Simulation using diesel fuel injection system parts production data.
• The Pinning Control (PC) strategy demonstrated an average reduction of 29.83% in Recovery Time Steps (RTS) compared to Self-adaption Control (SAC).
• The PC strategy achieved an average reduction of 46.9% in Node Failure Times (NFT) compared to SAC across varying disturbance intensities.

Conclusions:

• The proposed pinning control strategy offers significant advantages in managing the duration and scale of disturbance propagation in production networks.
• The developed model and control strategies provide a viable approach for enhancing stability in complex manufacturing systems.
• The evaluation indexes RTS and NFT effectively quantify the performance of different stability control strategies.