Real-Time Feasibility of Digital Twins for Process Control: A Computational Analysis

Digital twins enable closed-loop process control in smart manufacturing, yet no quantitative mapping exists between controller computational complexity and achievable real-time performance class. This paper aims to establish a quantitative mapping between controller computational complexity and achievable real-time performance class in digital twin-based process control, providing evidence-based deployment guidance for smart manufacturing. Three controller architectures—proportional–integral–derivative, model predictive control, and its robust variant—are implemented and timed on a finite-difference state-space model of a 1 mm steel slab under boundary heat flux, representative of laser-based and induction heating in manufacturing. Per-cycle latency is characterized through time series, cumulative distribution analysis, and deadline-miss rate on standard hardware without real-time operating system support. The proportional–integral–derivative controller satisfies hard real-time constraints with sub-0.05 ms latency; model predictive control with warm-starting achieves a 99th-percentile latency of 2.43 ms against a 10 ms deadline with zero misses across all tested prediction horizons. Robust model predictive control yields a mean latency of 770 ms—154 times the 5 ms control period—placing it firmly in the near-real-time class. A robust linear matrix inequality delay-margin analysis certifies closed-loop stability bounds across three uncertainty scenarios as a function of actuation delay; a finite-horizon induced-gain metric reveals a worst-case disturbance amplification peak near 100 control steps. Model predictive control is shown to compensate for actuation delays up to 50 ms that destabilize proportional–integral control, establishing it as the preferred architecture in latency-constrained digital twin deployments.