Modeling and Adaptive Event-Triggered Control for Coupled Re-Entrant Manufacturing Networks

This paper delves into the demand tracking control problem for a class of coupled re-entrant manufacturing networks (RMNs) utilizing adaptive event-triggered control strategies. The network modeling progresses hierarchically: 1) characterizing the local mass conservation law via explicit dynamical equation at the individual node level; 2) extending to the global mass conservation law for a single RMN; 3) generalizing to a comprehensive global mass balance formulation for a class of coupled RMNs. This multi-scale modeling strategy, transitioning from the local node dynamics to the network-level interactions, yields a rigorous hyperbolic-PDE-based continuum approximation model that efficiently captures the spatiotemporal evolutions of production flows in interconnected manufacturing networks. To reduce unnecessary control actions while maintaining precise regulation of the work-in-progress (WIP) quantity, two novel adaptive event-triggered control schemes incorporating the node-level and the edge-level coordination mechanisms, respectively, are developed for the coupled RMNs. These adaptive triggering mechanisms dynamically adjust triggering thresholds and control gains online, with Lyapunov-based analysis providing sufficient conditions guaranteeing asymptotic tracking of the desired production profiles and exclusion of the Zeno behavior. Numerical simulations validate the effectiveness of the control schemes.