Model selection and parameter optimization of model predictive control for building radiant systems

• Published in: Journal of process control Vol. 154; p. 103512
• Main Authors: Chen, Qiong, Wang, Wenjing, Li, Nan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2025
• Subjects: Model order reduction (MOR); Model predictive control (MPC); Parameter optimization; Performance assessment; Radiant systems; Model predictive control (MPC); Radiant systems; Model order reduction (MOR); Parameter optimization; Performance assessment
• ISSN: 0959-1524
• DOI: 10.1016/j.jprocont.2025.103512

In this work, we present a comprehensive parametric investigation quantifying the influence of reduced-order models (ROMs) of varying fidelity on both dynamic and steady-state performance of a Model Predictive Control (MPC) loop for building thermal regulation. Through simulations using the full-order model and ROMs of orders 1–6, we systematically determined that ROMs of order 4 or higher achieve temperature overshoots within 0.2 °C, settling times under 15 min, and steady-state errors below 0.5 °C when paired with prediction horizons of 12–24 steps and control horizons ≥ 2, thus matching full-order MPC performance while reducing computation by up to 70 %. In contrast, the lowest-order ROM (ROM1) requires a prediction horizon ≤ 12 and a control horizon ≥ 3 to limit overshoot to 1.0 °C and static error to 1.2 °C. Furthermore, the original model and high-order ROMs maintain robust control (overshoot < 0.5 °C, settling time < 10 min) across manipulated-variable rate weights from 0.1 to 1.0 and manipulated-output weights from 0.5 to 2.0, whereas ROM1 exhibits strong sensitivity, operating acceptably only near MV-rate ≈ 0.2 and MO weight ≈ 1.0. These quantitative guidelines enable practitioners to balance computational cost and control accuracy by selecting an appropriately ordered ROM and tuning horizons and weightings within the identified numerical ranges. •The prediction model accuracy’s influence on MPC performance was analyzed.•Parametric simulations for MPC parameter tuning were conducted.•ROMs-based MPC performance with different orders was investigated.•The prediction horizon, control horizon, weights of MV rate and MO were tuned for MPC.