## 1 Introduction  
Despite the increasing adoption of renewable energy in modern power grids, supercritical coal-fired power plants still play a key role in modern energy systems, offering higher thermal efficiency and lower emissions compared to traditional subcritical designs [1]. However, the complex boiler-turbine systems of these plants pose significant control challenges due to their strong multivariable coupling, large inertia, and obvious nonlinearity [2]. Traditional control strategies, such as proportional-integral-derivative (PID) controllers, have been widely employed [2, 3]. While effective in simpler systems, PID controllers struggle to manage the intricacies of supercritical units, particularly under variable operating conditions. Time delays, parameter variations, and external disturbances often lead to suboptimal performance. These limitations have prompted the exploration of more advanced solutions, with Model Predictive Control (MPC) emerging as a promising solution. MPC leverages a predictive framework to optimize control actions while respecting operational constraints, making it well suited to handling the multivariable interactions and nonlinearities inherent in power plants [4]. Researchers have developed various MPC variants to tackle specific challenges. [PERSON] et al. (2018) proposed an adaptive second-order non-singular terminal sliding mode control method, which effectively solved the lumped disturbance problem in nuclear power plant power control. [PERSON] et al. (2018) suggested a robust Model Predictive Control solution for the control problem caused by solar radiation fluctuations in solar thermal power plants, which solved the uncertainty problem well. [PERSON] et al. (2018) proposed a dual-timescale nonlinear Model Predictive Control strategy (T-NMPC) to address the problems of dynamic response timescale mismatch, nonlinear control problems, and the difficulty of coordination between flexibility and stability in the coupled operation of coal-fired power plants and carbon capture systems. [PERSON] et al. (2018) suggested a distributed Model Predictive Control algorithm based on logic to solve the problems of the high computational complexity of centralized Model Predictive Control (MPC) in parabolic trough thermal power field thermal power optimization, difficulties in the real-time control of large- and medium-sized power stations and the insufficient performance of local controllers. However, MPC's effectiveness hinges on the quality of its underlying dynamic model; traditional modeling methods such as transfer function ([PERSON] et al., 2016) and state space ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018) representation find it difficult to fully capture the nonlinear, time-varying dynamics of these systems, which complicates the development of precise control strategies. In order to enhance MPC's performance, researchers have incorporated filtering techniques and neural network-based system identification. Standard methods like the Kalman Filter (KF) ([PERSON], 1963; [PERSON], 1964; [PERSON], 1965) offer optimal state estimation for linear systems, but the nonlinear nature of supercritical plants has driven the adoption of advanced variants, such as the Extended Kalman Filter (EKF) ([PERSON], 1964). These approaches improve robustness by accounting for nonlinear dynamics and disturbances. Additionally, [PERSON] et al. (2018) proposed a Model Predictive Control (ESKF-MPC) strategy based on an extended-state Kalman Filter (ESKF) in view of the flexible peaking demand, energy-saving transformation problems, and control performance constrained by model uncertainty faced by coal-fired power plants with a high proportion of renewable energy that are under the grid. [PERSON] et al.