Modeling and Finite-Horizon MPC for a Boiler-Turbine System Using Minimal Realization State-Space Model

This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using system-identification method. Considering that the strong state coupling of mechanism model, subspace identification method is first utilized obtain linear state-space and transformed into an input–output model. By taking inputs outputs as states, augmented non-minimal (NMSS) measurable constructed. In order reduce computation burden, NMSS further canonical formulation by adopting Kalman decomposition. Based on minimal realization MPC controller parameterized optimization problem. Finally, simulations are performed evaluated performance proposed method, simulation results show that: approximate accurately; can achieve satisfactory stable performance; time 18.388 s overall problem also illustrates real-time effectively.