## 4 Conclusions  
This study proposes an Enhanced Error-Adaptive ESKF-MPC controller that significantly improves the control quality of supercritical power plants through hybrid modeling and dynamic error compensation. The main conclusions are as follows:  
1. The neural network-based identification method outperforms the traditional transfer function approach, achieving a higher accuracy with RMSE reductions of 30.6% for pressure and 27.4% for temperature. This highlights its potential for precise parameter identification in complex power systems. 2. The EEA-ESKF-MPC demonstrates fast response, minimal overshoot, and high robustness in handling wide load variations and command disturbances. Compared to ESKF-MPC, it reduces the load and temperature IAE by 3.05% and 2.46%, and the ME by 1.14% and 2.31%, respectively, with a slight 0.43% increase in the pressure IAE. In disturbance scenarios, it demonstrated the ability to balance out the errors of each control, enhancing the tracking accuracy and system smoothness. 3. Real condition tracking experiments using data from a 350 MW supercritical unit demonstrate substantial improvements over the plant's PID controller, with reductions \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **IAE1** & **IAE2** & **IAE3** & **ME1 (MPa)** & **ME2 (MW)** & **ME3 (\({}^{\circ}\)C)** \\ \hline Power plant controller & 2667.670 & 6010.110 & 23,025.860 & 1.560 & 6.860 & 41.780 \\ EEA-ESKF-MPC & 605.807 & 4384.299 & 8000.564 & 0.923 & 6.498 & 10.411 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of control results between power plant controller and EA-ESKF-MPC. in the IAE by 77.25% (pressure), 27.05% (load), and 65.25% (temperature), and in the ME by 40.83%, 5.30%, and 75.08%, respectively. These results affirm the EEA-ESKF-MPC's capability to enhance control quality under realistic operating conditions, offering practical benefits like enhanced efficiency and potential reductions in coal use. The EEA-ESKF-MPC offers robust scalability, enabling its adaptation to diverse power generation systems beyond supercritical units, such as ultra-supercritical units and combined cycle plants, which feature distinct thermodynamic properties and operational constraints; however, applying the method to these systems requires re-identification of the system model to capture their unique nonlinear dynamics and multivariable coupling, along with the recalibration of parameters like the adaptive weights and exponential smoothing factors to optimize control performance for their specific response characteristics. Although the controller has demonstrated promising results in simulations and real-condition tracking using data from a 350 MW supercritical unit, its deployment in an operational power plant remains unrealized, facing significant challenges including integration with existing DCS, which demands standardized communication protocols and compatibility with legacy hardware to ensure seamless operation, as well as rigorous safety certifications to comply with industry standards for critical control loops and real-time computational optimization to meet stringent response time requirements essential for plant reliability. Addressing these practical barriers is crucial to transition the EEA-ESKF-MPC from simulation to real-world application, and future work will focus on adaptively tuning additional parameters and optimizing weight assignments within the MPC framework to enhance control precision, while incorporating online optimization techniques, reinforcement learning, and semi-physical simulations to further improve practical applicability and robustness in diverse operational scenarios. Conceptualization, G.C., S.H. and L.S. ; methodology, G.C., C.F. and L.S. ; software, C.F. and C.W. ; validation, C.F. and S.W. ; formal analysis, G.C. ; investigation, G.C. ; resources, S.H. ; data curation, C.W. ; writing--original draft preparation, G.C. ; writing--review and editing, S.H. ; visualization, C.F. ; supervision, L.S. ; project administration, G.C. and L.S.