Temperature Control of Thermal Performance Testing Systems Based on an Adaptive PI–RLS–MPC Strategy

Accurate thermal conductivity measurement requires temperature control systems to establish stable operating conditions within a limited time. In practical thermal conductivity performance testing systems, large thermal inertia, complex heat transfer paths, and input time delays arising from thermal propagation and sensor placement often degrade dynamic response and control accuracy. To overcome these limitations, a composite PI–RLS–MPC control strategy is proposed for thermal systems with inertia and time delay. A proportional–integral (PI) controller serves as the baseline stabilizing controller, while model predictive control (MPC) is utilized to optimize the control input by explicitly considering system delay and input constraints. To enhance robustness against model uncertainty and parameter variations, recursive least squares (RLS) is adopted for online parameter identification and adaptive PI tuning, and a steady-state parameter freezing mechanism is introduced to suppress unnecessary parameter updates after convergence. Simulation studies are performed on an identified thermal process model with a 20 s input time delay. The results indicate that the proposed strategy reduces overshoot, shortens settling time, and improves disturbance rejection compared with conventional controllers. Overall, the proposed PI–RLS–MPC approach provides a practical solution for improving temperature control performance in thermal conductivity testing systems.