ORDER-REDUCED CONSTRAINED GPC ALGORITHM WITH WAVELET-BASED FEATURE EXTRACTION

摘要

Model predictive control(MPC) has a promising prospect in metallurgical process control. In this work, the main features of a high-order process are represented by a low frequency component of a response curve of the process based on wavelet decomposition, and then the model order of the process is reduced by approaching the low frequency component with a low-order model. Subsequently, the high-order process is controlled by employing the order-reduced model and a constrained generalized predictive control(GPC) algorithm with simple feedback correction. Lastly, the feasibility and effectiveness are confirmed by a simulation example.