Model Predictive Control for Industrial Automation: Enhancing Efficiency and Stability in Smart Manufacturing Systems
Keywords:
Model Predictive Control (MPC); Industrial Automation; Smart Manufacturing; Digital Twin Simulation; Efficiency–Stability Co-Optimization.Abstract
Smart manufacturing systems require controllers that maintain product quality and closed-loop regularity while limiting energy demand, actuator motion, and constraint violations. This paper presents a constraint-aware Model Predictive Control (MPC) formulation for a virtual manufacturing process benchmark. The proposed controller combines tracking, control effort, actuator-smoothness, energy, terminal, and slack-penalty terms in a single receding-horizon quadratic program. The contribution is positioned as an integrated simulation-based MPC design and evaluation framework rather than as a new general stability theorem or a plant-validated digital twin. The paper specifies the continuous and discrete process models, delay implementation, disturbance and noise assumptions, energy calculation, controller constraints, tuning procedure, benchmark-controller settings, and performance metrics to improve reproducibility. The proposed controller is compared with PID, Fuzzy PID, and a standard constrained MPC under nominal set-point tracking, load-disturbance rejection, infeasible-reference constraint handling, and 20% model mismatch. In the nominal case, the proposed MPC reduced IAE from 151.3314 to 75.4142, a 50.17% reduction relative to PID. Under load disturbance and model mismatch, IAE reductions relative to PID were 57.17% and 55.62%, respectively. Across energy-reporting scenarios, total energy consumption decreased by approximately 32.65% relative to PID. In the constrained scenario, the proposed MPC eliminated output-constraint violation time while explicitly accepting higher raw tracking error relative to the infeasible reference. The results indicate that energy-aware smoothing and soft-constraint handling can improve the efficiency-regularity trade-off in a low-order simulation benchmark, but hardware-in-the-loop and plant-level validation are required before broad industrial deployment claims can be made.
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