Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes

Nowadays, quickly changing customer demands are a big challenge in the manufacturing industry, especially for job shops, which typical coupling and nonlinear multi-input–multi-output (MIMO) systems. In order to achieve good shop floor performance presence of short-term demand fluctuations, key indicator—work process (WIP)—is required be effectively controlled vicinity desired levels. For this purpose, machinery-oriented capacity adjustment approach via reconfigurable machine tool (RMT) is employed flexibly balance load case bottleneck. A mathematical model concerning RMT WIP was first established uncertainty delays. The operator-based robust right coprime factorization (RRCF) method adopted stabilize uncertain system, adaptive integral separated proportional–integral (ISPI) tracking controllers were further designed improve transient robustness performance. proposed ISPI-RRCF analyzed compared with that state-of-the-art simulation. results showed both control systems could ensure within an allowed bound, while former had lower overshoots, shorter setting times, more concentrated distributions facing stochastic demands. This indicated effectiveness algorithm avoidance serious bottlenecks unbalanced distributions.