Abstract
As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions.